diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml index 42e9c103c..4e566d4be 100644 --- a/.github/workflows/deploy-docs.yml +++ b/.github/workflows/deploy-docs.yml @@ -25,7 +25,7 @@ jobs: cache-dependency-path: ./repo/docs/package-lock.json - name: Install dependencies - run: npm install --frozen-lockfile + run: npm install working-directory: ./repo/docs - name: Build website diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 674abfca9..46ec82fa2 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -49,21 +49,31 @@ jobs: run: | git config user.name release-bot git config user.email release-bot@ingonyama.com - cargo workspaces version ${{ inputs.releaseType }} -y --no-individual-tags -m "Bump rust crates' version" + cargo workspaces version ${{ inputs.releaseType }} -y --no-individual-tags --no-git-push -m "Bump rust crates' version" + - name: Bump docs version + id: bump-docs-version + if: inputs.releaseType != 'patch' + working-directory: ./docs + run: | + LATEST_TAG=$(git describe --tags --abbrev=0) + LATEST_VERSION=$LATEST_TAG:0:1 + npm run docusaurus docs:version $LATEST_VERSION + git add --all + git commit -m "Bump docs version" + git push - name: Create draft release env: GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: | - mkdir -p release_output && rm -rf ./release_output/* - # skip building release tar. TODO Yuval fix - # ./scripts/release/build_all.sh ./release_output LATEST_TAG=$(git describe --tags --abbrev=0) gh release create $LATEST_TAG --generate-notes -d --verify-tag -t "Release $LATEST_TAG" - name: Upload release tars env: GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: | + mkdir -p release_output && rm -rf ./release_output/* LATEST_TAG=$(git describe --tags --abbrev=0) + ./scripts/release/build_all.sh ./release_output $LATEST_TAG for file in ./release_output/*.tar.gz; do gh release upload $LATEST_TAG "$file" done diff --git a/.github/workflows/test-deploy-docs.yml b/.github/workflows/test-deploy-docs.yml index 47550b2db..cc9079ae5 100644 --- a/.github/workflows/test-deploy-docs.yml +++ b/.github/workflows/test-deploy-docs.yml @@ -22,7 +22,7 @@ jobs: cache-dependency-path: ./repo/docs/package-lock.json - name: Install dependencies - run: npm install --frozen-lockfile + run: npm install working-directory: ./repo/docs - name: Test build website run: npm run build diff --git a/docs/.gitignore b/docs/.gitignore index 984e65646..0c0d97324 100644 --- a/docs/.gitignore +++ b/docs/.gitignore @@ -2,6 +2,7 @@ node_modules/ yarn.lock .DS_Store +build/ # tex build artifacts .aux diff --git a/docs/docs/icicle/colab-instructions.md b/docs/docs/icicle/colab-instructions.md index e81bb3907..ff2f3d45f 100644 --- a/docs/docs/icicle/colab-instructions.md +++ b/docs/docs/icicle/colab-instructions.md @@ -9,11 +9,11 @@ First thing to do in a notebook is to set the runtime type to a T4 GPU. - in the upper corner click on the dropdown menu and select "change runtime type" -![Change runtime](../../static/img/colab_change_runtime.png) +![Change runtime](./static/img/colab_change_runtime.png) - In the window select "T4 GPU" and press Save -![T4 GPU](../../static/img/t4_gpu.png) +![T4 GPU](./static/img/t4_gpu.png) Installing Rust is rather simple, just execute the following command: diff --git a/docs/docs/icicle/golang-bindings.md b/docs/docs/icicle/golang-bindings.md index c279b1cf4..87f95a439 100644 --- a/docs/docs/icicle/golang-bindings.md +++ b/docs/docs/icicle/golang-bindings.md @@ -54,7 +54,7 @@ If more than one curve or more than one field is supplied, the last one supplied ::: -To build ICICLE libraries for all supported curves without certain features, you can use their -skip_ flags. For example, for disabling G2 and ECNTT: +To build ICICLE libraries for all supported curves without certain features, you can use their -skip_\ flags. For example, for disabling G2 and ECNTT: ```bash ./build.sh -curve=all -skip_g2 -skip_ecntt diff --git a/docs/docs/icicle/integrations.md b/docs/docs/icicle/integrations.md index e628894b6..bb37a7284 100644 --- a/docs/docs/icicle/integrations.md +++ b/docs/docs/icicle/integrations.md @@ -10,7 +10,7 @@ If you're interested in understanding these integrations better or learning how Lets illustrate an ICICLE integration, so you can understand the core API and design overview of ICICLE. -![ICICLE architecture](../../static/img/architecture-high-level.png) +![ICICLE architecture](./static/img/architecture-high-level.png) Engineers usually use a cryptographic library to implement their ZK protocols. These libraries implement efficient primitives which are used as building blocks for the protocol; ICICLE is such a library. The difference is that ICICLE is designed from the start to run on GPUs; the Rust and Golang APIs abstract away all low level CUDA details. Our goal was to allow developers with no GPU experience to quickly get started with ICICLE. diff --git a/docs/docs/icicle/overview.md b/docs/docs/icicle/overview.md index 42557e7ea..b7663e163 100644 --- a/docs/docs/icicle/overview.md +++ b/docs/docs/icicle/overview.md @@ -9,11 +9,8 @@ title: ICICLE Overview Untitled design (29)

- - ## What is ICICLE? - [![GitHub Release](https://img.shields.io/github/v/release/ingonyama-zk/icicle)](https://github.com/ingonyama-zk/icicle/releases) [ICICLE](https://github.com/ingonyama-zk/icicle) is a cryptography library designed to accelerate zero-knowledge proofs (ZKPs) using multiple compute backends, including GPUs, CPUs, and potentially other platforms. ICICLE's key strength lies in its ability to implement blazing-fast cryptographic primitives, enabling developers to significantly reduce proving times with minimal effort. @@ -78,7 +75,10 @@ ICICLE is also well-suited for prototyping and developing small-scale projects. Explore the full capabilities of ICICLE by diving into the [Architecture](./arch_overview.md), [Getting Started Guide](./getting_started.md) and the [Programmer's Guide](./programmers_guide/general.md) to learn how to integrate, deploy, and extend ICICLE across different backends. -If you have any questions or need support, feel free to reach out on [Discord], [GitHub](https://github.com/ingonyama-zk) or . We're here to help you accelerate your ZK development with ICICLE. +If you have any questions or need support, feel free to reach out on [Discord], [GitHub] or [via support email][SupportEmail]. We're here to help you accelerate your ZK development with ICICLE. -[ICICLE-OVERVIEW]: ./icicle/overview.md + [Discord]: https://discord.gg/6vYrE7waPj +[Github]: https://github.com/ingonyama-zk +[SupportEmail]: mailto:support@ingonyama.com + diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js deleted file mode 100644 index 61835f0b8..000000000 --- a/docs/docusaurus.config.js +++ /dev/null @@ -1,167 +0,0 @@ -// @ts-check -const lightCodeTheme = require('prism-react-renderer/themes/github'); -const darkCodeTheme = require('prism-react-renderer/themes/dracula'); -const math = require('remark-math'); -const katex = require('rehype-katex'); - -/** @type {import('@docusaurus/types').Config} */ -const config = { - title: 'Ingonyama Developer Documentation', - tagline: 'Ingonyama is a next-generation semiconductor company, focusing on Zero-Knowledge Proof hardware acceleration. We build accelerators for advanced cryptography, unlocking real-time applications.', - url: 'https://dev.ingonyama.com/', - baseUrl: '/', - onBrokenLinks: 'throw', - onBrokenMarkdownLinks: 'warn', - favicon: 'img/logo.png', - organizationName: 'ingonyama-zk', - projectName: 'developer-docs', - trailingSlash: false, - deploymentBranch: "main", - presets: [ - [ - 'classic', - /** @type {import('@docusaurus/preset-classic').Options} */ - ({ - docs: { - showLastUpdateAuthor: true, - showLastUpdateTime: true, - routeBasePath: '/', - remarkPlugins: [math, require('mdx-mermaid')], - rehypePlugins: [katex], - sidebarPath: require.resolve('./sidebars.js'), - editUrl: 'https://github.com/ingonyama-zk/icicle/tree/main/docs', - }, - blog: { - remarkPlugins: [math, require('mdx-mermaid')], - rehypePlugins: [katex], - showReadingTime: true, - editUrl: 'https://github.com/ingonyama-zk/icicle/tree/main', - }, - pages: {}, - theme: { - customCss: require.resolve('./src/css/custom.css'), - }, - }), - ], - ], - - stylesheets: [ - { - href: 'https://cdn.jsdelivr.net/npm/katex@0.13.24/dist/katex.min.css', - type: 'text/css', - integrity: - 'sha384-odtC+0UGzzFL/6PNoE8rX/SPcQDXBJ+uRepguP4QkPCm2LBxH3FA3y+fKSiJ+AmM', - crossorigin: 'anonymous', - }, - ], - - scripts: [ - { - src: 'https://plausible.io/js/script.js', - 'data-domain':'ingonyama.com', - defer: true, - }, - ], - - themeConfig: - /** @type {import('@docusaurus/preset-classic').ThemeConfig} */ - ({ - metadata: [ - {name: 'twitter:card', content: 'summary_large_image'}, - {name: 'twitter:site', content: '@Ingo_zk'}, - {name: 'twitter:title', content: 'Ingonyama Developer Documentation'}, - {name: 'twitter:description', content: 'Ingonyama is a next-generation semiconductor company focusing on Zero-Knowledge Proof hardware acceleration...'}, - {name: 'twitter:image', content: 'https://dev.ingonyama.com/img/logo.png'}, - // title - {name: 'og:title', content: 'Ingonyama Developer Documentation'}, - {name: 'og:description', content: 'Ingonyama is a next-generation semiconductor company focusing on Zero-Knowledge Proof hardware acceleration...'}, - {name: 'og:image', content: 'https://dev.ingonyama.com/img/logo.png'}, - ], - hideableSidebar: true, - colorMode: { - defaultMode: 'dark', - respectPrefersColorScheme: false, - }, - algolia: { - appId: 'PZY4KJBBBK', - apiKey: '2cc940a6e0ef5c117f4f44e7f4e6e20b', - indexName: 'ingonyama', - contextualSearch: true, - externalUrlRegex: 'external\\.com|domain\\.com', - replaceSearchResultPathname: { - from: '/docs/', - to: '/', - }, - searchParameters: {}, - searchPagePath: 'search', - }, - navbar: { - title: 'Ingonyama Developer Documentation', - logo: { - alt: 'Ingonyama Logo', - src: 'img/logo.png', - }, - items: [ - { - position: 'left', - label: 'Docs', - to: '/', - }, - { - href: 'https://github.com/ingonyama-zk', - position: 'right', - label: 'GitHub', - }, - { - href: 'https://www.ingonyama.com/ingopedia/glossary', - position: 'right', - label: 'Ingopedia', - }, - { - type: 'dropdown', - position: 'right', - label: 'Community', - items: [ - { - label: 'Discord', - href: 'https://discord.gg/6vYrE7waPj', - }, - { - label: 'Twitter', - href: 'https://x.com/Ingo_zk', - }, - { - label: 'YouTube', - href: 'https://www.youtube.com/@ingo_ZK' - }, - { - label: 'Mailing List', - href: 'https://wkf.ms/3LKCbdj', - } - ] - }, - - ], - }, - footer: { - copyright: `Copyright © ${new Date().getFullYear()} Ingonyama, Inc. Built with Docusaurus.`, - }, - prism: { - theme: lightCodeTheme, - darkTheme: darkCodeTheme, - additionalLanguages: ['rust', 'go'], - }, - image: 'img/logo.png', - announcementBar: { - id: 'announcement', // Any value that will identify this message. - content: - '❄️🎉 New Release! ICICLE v3.1! Click here for the full update 🎉❄️', - backgroundColor: '#64f5ef', // Light blue background color. - textColor: '#000000', // Black text color. - isCloseable: true, // Defaults to `true`. - }, - }), -}; - -module.exports = config; - diff --git a/docs/docusaurus.config.ts b/docs/docusaurus.config.ts new file mode 100644 index 000000000..4e2fb4214 --- /dev/null +++ b/docs/docusaurus.config.ts @@ -0,0 +1,186 @@ +import type { Config } from '@docusaurus/types'; +import type * as Preset from '@docusaurus/preset-classic'; +import type { NavbarItem } from '@docusaurus/theme-common'; + +import { themes } from 'prism-react-renderer'; +import math from 'remark-math'; +import katex from 'rehype-katex'; + +const lightCodeTheme = themes.github; +const darkCodeTheme = themes.dracula; + +const ingoPreset = { + docs: { + showLastUpdateAuthor: true, + showLastUpdateTime: true, + includeCurrentVersion: false, + routeBasePath: '/', + remarkPlugins: [math], + rehypePlugins: [katex], + sidebarPath: require.resolve('./sidebars.ts'), + editUrl: 'https://github.com/ingonyama-zk/icicle/tree/main/docs', + }, + blog: { + remarkPlugins: [math], + rehypePlugins: [katex], + showReadingTime: true, + editUrl: 'https://github.com/ingonyama-zk/icicle/tree/main', + }, + pages: {}, + theme: { + customCss: require.resolve('./src/css/custom.css'), + }, +} satisfies Preset.Options + +const navBarLeftSide = [ + { + position: 'left', + label: 'Docs', + to: '/', + } +] satisfies NavbarItem[] + +const navBarRightSide = [ + { + type: 'docsVersionDropdown', + position: 'right', + dropdownActiveClassDisabled: true, + }, + { + href: 'https://github.com/ingonyama-zk', + position: 'right', + label: 'GitHub', + }, + { + href: 'https://www.ingonyama.com/ingopedia/glossary', + position: 'right', + label: 'Ingopedia', + }, + { + type: 'dropdown', + position: 'right', + label: 'Community', + items: [ + { + label: 'Discord', + href: 'https://discord.gg/6vYrE7waPj', + }, + { + label: 'Twitter', + href: 'https://x.com/Ingo_zk', + }, + { + label: 'YouTube', + href: 'https://www.youtube.com/@ingo_ZK' + }, + { + label: 'Mailing List', + href: 'https://wkf.ms/3LKCbdj', + } + ] + } +] satisfies NavbarItem[] + +const config: Config = { + title: 'Ingonyama Developer Documentation', + tagline: 'Ingonyama is a next-generation semiconductor company, focusing on Zero-Knowledge Proof hardware acceleration. We build accelerators for advanced cryptography, unlocking real-time applications.', + url: 'https://dev.ingonyama.com/', + baseUrl: '/', + onBrokenLinks: 'throw', + onBrokenMarkdownLinks: 'warn', + favicon: 'img/logo.png', + organizationName: 'ingonyama-zk', + projectName: 'developer-docs', + trailingSlash: false, + deploymentBranch: "main", + staticDirectories: ['static'], + presets: [ + [ + 'classic', + ingoPreset, + ], + ], + stylesheets: [ + { + href: 'https://cdn.jsdelivr.net/npm/katex@0.13.24/dist/katex.min.css', + type: 'text/css', + integrity: + 'sha384-odtC+0UGzzFL/6PNoE8rX/SPcQDXBJ+uRepguP4QkPCm2LBxH3FA3y+fKSiJ+AmM', + crossorigin: 'anonymous', + }, + ], + + scripts: [ + { + src: 'https://plausible.io/js/script.js', + 'data-domain':'ingonyama.com', + defer: true, + }, + ], + + themeConfig:{ + metadata: [ + {name: 'twitter:card', content: 'summary_large_image'}, + {name: 'twitter:site', content: '@Ingo_zk'}, + {name: 'twitter:title', content: 'Ingonyama Developer Documentation'}, + {name: 'twitter:description', content: 'Ingonyama is a next-generation semiconductor company focusing on Zero-Knowledge Proof hardware acceleration...'}, + {name: 'twitter:image', content: 'https://dev.ingonyama.com/img/logo.png'}, + // title + {name: 'og:title', content: 'Ingonyama Developer Documentation'}, + {name: 'og:description', content: 'Ingonyama is a next-generation semiconductor company focusing on Zero-Knowledge Proof hardware acceleration...'}, + {name: 'og:image', content: 'https://dev.ingonyama.com/img/logo.png'}, + ], + docs: { + sidebar: { + hideable: true, + } + }, + colorMode: { + defaultMode: 'dark', + respectPrefersColorScheme: false, + }, + algolia: { + appId: 'PZY4KJBBBK', + apiKey: '2cc940a6e0ef5c117f4f44e7f4e6e20b', + indexName: 'ingonyama', + contextualSearch: true, + externalUrlRegex: 'external\\.com|domain\\.com', + replaceSearchResultPathname: { + from: '/docs/', + to: '/', + }, + searchParameters: {}, + searchPagePath: 'search', + }, + navbar: { + title: 'Ingonyama Developer Documentation', + logo: { + alt: 'Ingonyama Logo', + src: 'img/logo.png', + }, + items: [ + ...navBarLeftSide, + ...navBarRightSide, + ], + }, + footer: { + copyright: `Copyright © ${new Date().getFullYear()} Ingonyama, Inc. Built with Docusaurus.`, + }, + prism: { + theme: lightCodeTheme, + darkTheme: darkCodeTheme, + additionalLanguages: ['rust', 'go'], + }, + image: 'img/logo.png', + announcementBar: { + id: 'announcement', // Any value that will identify this message. + content: + '❄️🎉 New Release! ICICLE v3.1! Click here for the full update 🎉❄️', + backgroundColor: '#64f5ef', // Light blue background color. + textColor: '#000000', // Black text color. + isCloseable: true, // Defaults to `true`. + }, + } satisfies Preset.ThemeConfig, +}; + +export default config; diff --git a/docs/package-lock.json b/docs/package-lock.json index fbdbe8aa0..77b8f9d3a 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -1,56 +1,64 @@ { - "name": "docusaurus", - "version": "0.0.0", - "lockfileVersion": 2, + "name": "icicle-docs", + "version": "3.1.0", + "lockfileVersion": 3, "requires": true, "packages": { "": { - "name": "docusaurus", - "version": "0.0.0", - "dependencies": { - "@docusaurus/core": "2.0.0-beta.18", - "@docusaurus/preset-classic": "2.0.0-beta.18", - "@mdx-js/react": "^1.6.22", - "clsx": "^1.1.1", - "hast-util-is-element": "1.1.0", - "mdx-mermaid": "^1.2.2", - "mermaid": "^9.1.2", - "prism-react-renderer": "^1.3.1", - "react": "^17.0.2", - "react-dom": "^17.0.2", - "rehype-katex": "5", - 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b/docs/package.json @@ -1,6 +1,6 @@ { - "name": "docusaurus", - "version": "0.0.0", + "name": "icicle-docs", + "version": "3.1.0", "private": true, "description": "Ingonyama - developer docs", "scripts": { @@ -17,18 +17,16 @@ "format": "prettier --write '**/*.md'" }, "dependencies": { - "@docusaurus/core": "2.0.0-beta.18", - "@docusaurus/preset-classic": "2.0.0-beta.18", - "@mdx-js/react": "^1.6.22", - "clsx": "^1.1.1", - "hast-util-is-element": "1.1.0", - "mdx-mermaid": "^1.2.2", - "mermaid": "^9.1.2", - "prism-react-renderer": "^1.3.1", - "react": "^17.0.2", - "react-dom": "^17.0.2", - "rehype-katex": "5", - "remark-math": "3" + "@docusaurus/core": "3.6.2", + "@docusaurus/preset-classic": "3.6.2", + "@mdx-js/react": "~3.0.0", + "clsx": "~1.1.1", + "hast-util-is-element": "~1.1.0", + "prism-react-renderer": "~2.1.0", + "react": "~18.2.0", + "react-dom": "~18.2.0", + "rehype-katex": "~7", + "remark-math": "~6" }, "browserslist": { "production": [ @@ -43,6 +41,13 @@ ] }, "devDependencies": { - "prettier": "^3.2.4" + "@docusaurus/module-type-aliases": "~3.0.0", + "@docusaurus/tsconfig": "~3.6.1", + "@docusaurus/types": "~3.0.0", + "prettier": "~3.2.4", + "typescript": "~5.6.3" + }, + "engines": { + "node": ">=18.0" } } diff --git a/docs/sandbox.config.json b/docs/sandbox.config.json index 95df40889..fe547f732 100644 --- a/docs/sandbox.config.json +++ b/docs/sandbox.config.json @@ -3,8 +3,8 @@ "hardReloadOnChange": true, "view": "browser", "template": "docusaurus", - "node": "14", + "node": "18", "container": { - "node": "14" + "node": "18" } } diff --git a/docs/sidebars.js b/docs/sidebars.js deleted file mode 100644 index 316d1e761..000000000 --- a/docs/sidebars.js +++ /dev/null @@ -1,305 +0,0 @@ -module.exports = { - GettingStartedSidebar: [ - { - type: "doc", - label: "Introduction", - id: "introduction", - }, - { - type: "category", - label: "ICICLE", - link: { - type: `doc`, - id: 'icicle/overview', - }, - collapsed: false, - items: [ - { - type: "category", - label: "Getting started", - link: { - type: `doc`, - id: "icicle/getting_started", - }, - collapsed: false, - items: [ - { - type: "doc", - label: "Build ICICLE from source", - id: "icicle/build_from_source", - }, - { - type: "category", - label: "Programmers guide", - link: { - type: `doc`, - id: "icicle/programmers_guide/general", - }, - collapsed: false, - items: [ - { - type: "doc", - label: "C++", - id: "icicle/programmers_guide/cpp", - }, - { - type: "doc", - label: "Rust", - id: "icicle/programmers_guide/rust", - }, - { - type: "doc", - label: "Go", - id: "icicle/programmers_guide/go", - } - ], - }, - ], - }, - { - type: "category", - label: "Architecture overview", - link: { - type: `doc`, - id: "icicle/arch_overview" - }, - collapsed: false, - items: [ - { - type: "doc", - label: "CUDA Backend", - id: "icicle/install_cuda_backend" - }, - { - type: "doc", - label: "Multi-Device Support", - id: "icicle/multi-device", - }, - // { - // type: "doc", - // label: "Build Your Own Backend", - // id: "icicle/build_your_own_backend" - // }, - ] - }, - { - type: "doc", - label: "ICICLE libraries", - id: "icicle/libraries", - }, - { - type: "category", - label: "Compute API", - link: { - type: `doc`, - id: 'icicle/primitives/overview', - }, - collapsed: true, - items: [ - { - type: "doc", - label: "MSM", - id: "icicle/primitives/msm", - }, - { - type: "doc", - label: "NTT / ECNTT", - id: "icicle/primitives/ntt", - }, - { - type: "doc", - label: "Vector operations", - id: "icicle/primitives/vec_ops", - }, - { - type: "doc", - label: "Polynomials", - id: "icicle/polynomials/overview", - }, - { - type: "doc", - label: "Hash", - id: "icicle/primitives/hash", - }, - { - type: "doc", - label: "Merkle-Tree", - id: "icicle/primitives/merkle", - }, - { - type: "category", - label: "Golang bindings", - link: { - type: `doc`, - id: "icicle/golang-bindings", - }, - collapsed: true, - items: [ - { - type: "category", - label: "MSM", - link: { - type: `doc`, - id: "icicle/golang-bindings/msm", - }, - collapsed: true, - items: [ - { - type: "doc", - label: "MSM pre computation", - id: "icicle/golang-bindings/msm-pre-computation", - } - ] - }, - { - type: "doc", - label: "NTT", - id: "icicle/golang-bindings/ntt", - }, - { - type: "doc", - label: "EC-NTT", - id: "icicle/golang-bindings/ecntt", - }, - { - type: "doc", - label: "Vector operations", - id: "icicle/golang-bindings/vec-ops", - }, - { - type: "doc", - label: "Multi GPU Support", - id: "icicle/golang-bindings/multi-gpu", - }, - { - type: "doc", - label: "Hash", - id: "icicle/golang-bindings/hash", - }, - { - type: "doc", - label: "Merkle-Tree", - id: "icicle/golang-bindings/merkle", - }, - ] - }, - { - type: "category", - label: "Rust bindings", - link: { - type: `doc`, - id: "icicle/rust-bindings", - }, - collapsed: true, - items: [ - { - type: "doc", - label: "MSM", - id: "icicle/rust-bindings/msm", - }, - { - type: "doc", - label: "NTT", - id: "icicle/rust-bindings/ntt", - }, - { - type: "doc", - label: "ECNTT", - id: "icicle/rust-bindings/ecntt", - }, - { - type: "doc", - label: "Vector operations", - id: "icicle/rust-bindings/vec-ops", - }, - { - type: "doc", - label: "Polynomials", - id: "icicle/rust-bindings/polynomials", - }, - { - type: "doc", - label: "Hash", - id: "icicle/rust-bindings/hash", - }, - { - type: "doc", - label: "Merkle-Tree", - id: "icicle/rust-bindings/merkle", - }, - // { - // type: "doc", - // label: "Multi GPU Support (TODO)", - // id: "icicle/rust-bindings/multi-gpu", - // }, - ], - }, - ], - }, - { - type: "doc", - label: "Migrate from ICICLE v2", - id: "icicle/migrate_from_v2", - }, - // { - // type: "doc", - // label: "Benchmarks", - // id: "icicle/benchmarks", - // }, - // { - // type: "doc", - // label: "FAQ and Troubleshooting", - // id: "icicle/faq_and_troubleshooting", - // }, - { - type: "doc", - label: "Google Colab Instructions", - id: "icicle/colab-instructions", - }, - { - type: "doc", - label: "ICICLE Provers", - id: "icicle/integrations" - }, - ] - }, - { - type: "doc", - label: "Ingonyama Grant program", - id: "grants", - }, - { - type: "doc", - label: "Contributor guide", - id: "contributor-guide", - }, - { - type: "category", - label: "Additional Resources", - collapsed: false, - collapsible: false, - items: [ - { - type: "link", - label: "YouTube", - href: "https://www.youtube.com/@ingo_ZK" - }, - { - type: "link", - label: "Ingonyama Blog", - href: "https://www.ingonyama.com/blog" - }, - { - type: "link", - label: "Ingopedia", - href: "https://www.ingonyama.com/ingopedia" - }, - { - href: 'https://github.com/ingonyama-zk', - type: "link", - label: 'GitHub', - } - ] - } - ], -}; diff --git a/docs/sidebars.ts b/docs/sidebars.ts new file mode 100644 index 000000000..998d0bd80 --- /dev/null +++ b/docs/sidebars.ts @@ -0,0 +1,317 @@ +const gettingStartedDropdown = [ + { + type: "doc", + label: "Build ICICLE from source", + id: "icicle/build_from_source", + }, + { + type: "category", + label: "Programmers guide", + link: { + type: `doc`, + id: "icicle/programmers_guide/general", + }, + collapsed: false, + items: [ + { + type: "doc", + label: "C++", + id: "icicle/programmers_guide/cpp", + }, + { + type: "doc", + label: "Rust", + id: "icicle/programmers_guide/rust", + }, + { + type: "doc", + label: "Go", + id: "icicle/programmers_guide/go", + } + ], + }, +]; + +const architectureDropdown = [ + { + type: "doc", + label: "CUDA Backend", + id: "icicle/install_cuda_backend" + }, + { + type: "doc", + label: "Multi-Device Support", + id: "icicle/multi-device", + }, + // { + // type: "doc", + // label: "Build Your Own Backend", + // id: "icicle/build_your_own_backend" + // }, +]; + +const cppApi = [ + { + type: "doc", + label: "MSM", + id: "icicle/primitives/msm", + }, + { + type: "doc", + label: "NTT / ECNTT", + id: "icicle/primitives/ntt", + }, + { + type: "doc", + label: "Vector operations", + id: "icicle/primitives/vec_ops", + }, + { + type: "doc", + label: "Polynomials", + id: "icicle/polynomials/overview", + }, + { + type: "doc", + label: "Hash", + id: "icicle/primitives/hash", + }, + { + type: "doc", + label: "Merkle-Tree", + id: "icicle/primitives/merkle", + } +]; + +const golangBindingsApi = [ + { + type: "category", + label: "MSM", + link: { + type: `doc`, + id: "icicle/golang-bindings/msm", + }, + collapsed: true, + items: [ + { + type: "doc", + label: "MSM pre computation", + id: "icicle/golang-bindings/msm-pre-computation", + } + ] + }, + { + type: "doc", + label: "NTT", + id: "icicle/golang-bindings/ntt", + }, + { + type: "doc", + label: "EC-NTT", + id: "icicle/golang-bindings/ecntt", + }, + { + type: "doc", + label: "Vector operations", + id: "icicle/golang-bindings/vec-ops", + }, + { + type: "doc", + label: "Multi GPU Support", + id: "icicle/golang-bindings/multi-gpu", + }, + { + type: "doc", + label: "Hash", + id: "icicle/golang-bindings/hash", + }, + { + type: "doc", + label: "Merkle-Tree", + id: "icicle/golang-bindings/merkle", + } +]; + +const rustBindingsApi = [ + { + type: "doc", + label: "MSM", + id: "icicle/rust-bindings/msm", + }, + { + type: "doc", + label: "NTT", + id: "icicle/rust-bindings/ntt", + }, + { + type: "doc", + label: "ECNTT", + id: "icicle/rust-bindings/ecntt", + }, + { + type: "doc", + label: "Vector operations", + id: "icicle/rust-bindings/vec-ops", + }, + { + type: "doc", + label: "Polynomials", + id: "icicle/rust-bindings/polynomials", + }, + { + type: "doc", + label: "Hash", + id: "icicle/rust-bindings/hash", + }, + { + type: "doc", + label: "Merkle-Tree", + id: "icicle/rust-bindings/merkle", + }, + // { + // type: "doc", + // label: "Multi GPU Support (TODO)", + // id: "icicle/rust-bindings/multi-gpu", + // }, +]; + +export default { + GettingStartedSidebar: [ + { + type: "doc", + label: "Introduction", + id: "introduction", + }, + { + type: "category", + label: "ICICLE", + link: { + type: `doc`, + id: 'icicle/overview', + }, + collapsed: false, + items: [ + { + type: "category", + label: "Getting started", + link: { + type: `doc`, + id: "icicle/getting_started", + }, + collapsed: false, + items: gettingStartedDropdown + }, + { + type: "category", + label: "Architecture overview", + link: { + type: `doc`, + id: "icicle/arch_overview" + }, + collapsed: false, + items: architectureDropdown + }, + { + type: "doc", + label: "ICICLE libraries", + id: "icicle/libraries", + }, + { + type: "category", + label: "Compute API", + link: { + type: `doc`, + id: 'icicle/primitives/overview', + }, + collapsed: true, + items: [ + ...cppApi, + { + type: "category", + label: "Golang bindings", + link: { + type: `doc`, + id: "icicle/golang-bindings", + }, + collapsed: true, + items: golangBindingsApi + }, + { + type: "category", + label: "Rust bindings", + link: { + type: `doc`, + id: "icicle/rust-bindings", + }, + collapsed: true, + items: rustBindingsApi + }, + ], + }, + { + type: "doc", + label: "Migrate from ICICLE v2", + id: "icicle/migrate_from_v2", + }, + // { + // type: "doc", + // label: "Benchmarks", + // id: "icicle/benchmarks", + // }, + // { + // type: "doc", + // label: "FAQ and Troubleshooting", + // id: "icicle/faq_and_troubleshooting", + // }, + { + type: "doc", + label: "Google Colab Instructions", + id: "icicle/colab-instructions", + }, + { + type: "doc", + label: "ICICLE Provers", + id: "icicle/integrations" + }, + ] + }, + { + type: "doc", + label: "Ingonyama Grant program", + id: "grants", + }, + { + type: "doc", + label: "Contributor guide", + id: "contributor-guide", + }, + { + type: "category", + label: "Additional Resources", + collapsed: false, + collapsible: false, + items: [ + { + type: "link", + label: "YouTube", + href: "https://www.youtube.com/@ingo_ZK" + }, + { + type: "link", + label: "Ingonyama Blog", + href: "https://www.ingonyama.com/blog" + }, + { + type: "link", + label: "Ingopedia", + href: "https://www.ingonyama.com/ingopedia" + }, + { + href: 'https://github.com/ingonyama-zk', + type: "link", + label: 'GitHub', + } + ] + } + ], +}; diff --git a/docs/static/img/image.png b/docs/static/img/image.png new file mode 100644 index 000000000..9e6aecaaf Binary files /dev/null and b/docs/static/img/image.png differ diff --git a/docs/tsconfig.json b/docs/tsconfig.json new file mode 100644 index 000000000..230f200e8 --- /dev/null +++ b/docs/tsconfig.json @@ -0,0 +1,6 @@ +{ + "extends": "@docusaurus/tsconfig", + "compilerOptions": { + "baseUrl": "." + } +} \ No newline at end of file diff --git a/docs/docs/ZKContainers.md b/docs/versioned_docs/version-1.10.1/ZKContainers.md similarity index 90% rename from docs/docs/ZKContainers.md rename to docs/versioned_docs/version-1.10.1/ZKContainers.md index fb89fafd2..332c75cff 100644 --- a/docs/docs/ZKContainers.md +++ b/docs/versioned_docs/version-1.10.1/ZKContainers.md @@ -9,4 +9,4 @@ A ZKContainer is a standardized, optimized and secure docker container that we c ZKContainer has been used by Ingonyama clients to achieve scalability across large data centers. We suggest you read our [article](https://medium.com/@ingonyama/product-announcement-zk-containers-0e2a1f2d0a2b) regarding ZKContainer to understand the benefits of using them. -![ZKContainer inside a ZK data center](../static/img/architecture-zkcontainer.png) +![ZKContainer inside a ZK data center](/img/architecture-zkcontainer.png) diff --git a/docs/versioned_docs/version-1.10.1/contributor-guide.md b/docs/versioned_docs/version-1.10.1/contributor-guide.md new file mode 100644 index 000000000..ffd82cd9c --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/contributor-guide.md @@ -0,0 +1,23 @@ +# Contributor's Guide + +We welcome all contributions with open arms. At Ingonyama we take a village approach, believing it takes many hands and minds to build a ecosystem. + +## Contributing to ICICLE + +- Make suggestions or report bugs via [GitHub issues](https://github.com/ingonyama-zk/icicle/issues) +- Contribute to the ICICLE by opening a [pull request](https://github.com/ingonyama-zk/icicle/pulls). +- Contribute to our [documentation](https://github.com/ingonyama-zk/icicle/tree/main/docs) and [examples](https://github.com/ingonyama-zk/icicle/tree/main/examples). +- Ask questions on Discord + +### Opening a pull request + +When opening a [pull request](https://github.com/ingonyama-zk/icicle/pulls) please keep the following in mind. + +- `Clear Purpose` - The pull request should solve a single issue and be clean of any unrelated changes. +- `Clear description` - If the pull request is for a new feature describe what you built, why you added it and how its best that we test it. For bug fixes please describe the issue and the solution. +- `Consistent style` - Rust and Golang code should be linted by the official linters (golang fmt and rust fmt) and maintain a proper style. For CUDA and C++ code we use [`clang-format`](https://github.com/ingonyama-zk/icicle/blob/main/.clang-format), [here](https://github.com/ingonyama-zk/icicle/blob/605c25f9d22135c54ac49683b710fe2ce06e2300/.github/workflows/main-format.yml#L46) you can see how we run it. +- `Minimal Tests` - please add test which cover basic usage of your changes . + +## Questions? + +Find us on [Discord](https://discord.gg/6vYrE7waPj). diff --git a/docs/versioned_docs/version-1.10.1/grants.md b/docs/versioned_docs/version-1.10.1/grants.md new file mode 100644 index 000000000..1647d5400 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/grants.md @@ -0,0 +1,23 @@ +# Ingonyama Grant programs + +Ingonyama understands the importance of supporting and fostering a vibrant community of researchers and builders to advance ZK. To encourage progress, we are not only developing in the open but also sharing resources with researchers and builders through various programs. + +## ICICLE ZK-GPU Ecosystem Grant + +Ingonyama invites researchers and practitioners to collaborate in advancing ZK acceleration. We are allocating $100,000 for grants to support this initiative. + +### Bounties & Grants + +Eligibility for grants includes: + +1. **Students**: Utilize ICICLE in your research. +2. **Performance Improvement**: Enhance the performance of accelerated primitives in ICICLE. +3. **Protocol Porting**: Migrate existing ZK protocols to ICICLE. +4. **New Primitives**: Contribute new primitives to ICICLE. +5. **Benchmarking**: Compare ZK benchmarks against ICICLE. + +## Contact + +For questions or submissions: [grants@ingonyama.com](mailto:grants@ingonyama.com) + +**Read the full article [here](https://www.ingonyama.com/blog/icicle-for-researchers-grants-challenges)** diff --git a/docs/versioned_docs/version-1.10.1/icicle/colab-instructions.md b/docs/versioned_docs/version-1.10.1/icicle/colab-instructions.md new file mode 100644 index 000000000..d642dce4b --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/colab-instructions.md @@ -0,0 +1,138 @@ +# Run ICICLE on Google Colab + +Google Colab lets you use a GPU free of charge, it's an Nvidia T4 GPU with 16 GB of memory, capable of running latest CUDA (tested on Cuda 12.2) +As Colab is able to interact with shell commands, a user can also install a framework and load git repositories into Colab space. + +## Prepare Colab environment + +First thing to do in a notebook is to set the runtime type to a T4 GPU. + +- in the upper corner click on the dropdown menu and select "change runtime type" + +![Change runtime](/img/colab_change_runtime.png) + +- In the window select "T4 GPU" and press Save + +![T4 GPU](/img/t4_gpu.png) + +Installing Rust is rather simple, just execute the following command: + +```sh +!apt install rustc cargo +``` + +To test the installation of Rust: + +```sh +!rustc --version +!cargo --version +``` + +A successful installation will result in a rustc and cargo version print, a faulty installation will look like this: + +```sh +/bin/bash: line 1: rustc: command not found +/bin/bash: line 1: cargo: command not found +``` + +Now we will check the environment: + +```sh +!nvcc --version +!gcc --version +!cmake --version +!nvidia-smi +``` + +A correct environment should print the result with no bash errors for `nvidia-smi` command and result in a **Teslt T4 GPU** type: + +```sh +nvcc: NVIDIA (R) Cuda compiler driver +Copyright (c) 2005-2023 NVIDIA Corporation +Built on Tue_Aug_15_22:02:13_PDT_2023 +Cuda compilation tools, release 12.2, V12.2.140 +Build cuda_12.2.r12.2/compiler.33191640_0 +gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 +Copyright (C) 2021 Free Software Foundation, Inc. +This is free software; see the source for copying conditions. There is NO +warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. + +cmake version 3.27.9 + +CMake suite maintained and supported by Kitware (kitware.com/cmake). +Wed Jan 17 13:10:18 2024 ++---------------------------------------------------------------------------------------+ +| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 | +|-----------------------------------------+----------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+======================+======================| +| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 | +| N/A 39C P8 9W / 70W | 0MiB / 15360MiB | 0% Default | +| | | N/A | ++-----------------------------------------+----------------------+----------------------+ + ++---------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=======================================================================================| +| No running processes found | ++---------------------------------------------------------------------------------------+ +``` + +## Cloning ICICLE and running test + +Now we are ready to clone ICICE repository, + +```sh +!git clone https://github.com/ingonyama-zk/icicle.git +``` + +We now can browse the repository and run tests to check the runtime environment: + +```sh +!ls -la +%cd icicle +``` + +Let's run a test! +Navigate to icicle/wrappers/rust/icicle-curves/icicle-bn254 and run cargo test: + +```sh +%cd wrappers/rust/icicle-curves/icicle-bn254/ +!cargo test --release +``` + +:::note + +Compiling the first time may take a while + +::: + +Test run should end like this: + +```sh +running 15 tests +test curve::tests::test_ark_point_convert ... ok +test curve::tests::test_ark_scalar_convert ... ok +test curve::tests::test_affine_projective_convert ... ok +test curve::tests::test_point_equality ... ok +test curve::tests::test_field_convert_montgomery ... ok +test curve::tests::test_scalar_equality ... ok +test curve::tests::test_points_convert_montgomery ... ok +test msm::tests::test_msm ... ok +test msm::tests::test_msm_skewed_distributions ... ok +test ntt::tests::test_ntt ... ok +test ntt::tests::test_ntt_arbitrary_coset ... ok +test msm::tests::test_msm_batch has been running for over 60 seconds +test msm::tests::test_msm_batch ... ok +test ntt::tests::test_ntt_coset_from_subgroup ... ok +test ntt::tests::test_ntt_device_async ... ok +test ntt::tests::test_ntt_batch ... ok + +test result: ok. 15 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 99.39s +``` + +Viola, ICICLE in Colab! diff --git a/docs/versioned_docs/version-1.10.1/icicle/golang-bindings.md b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings.md new file mode 100644 index 000000000..6b832cdda --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings.md @@ -0,0 +1,105 @@ +# Golang bindings + +Golang bindings allow you to use ICICLE as a golang library. +The source code for all Golang libraries can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang). + +The Golang bindings are comprised of multiple packages. + +[`core`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/core) which defines all shared methods and structures, such as configuration structures, or memory slices. + +[`cuda-runtime`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/cuda_runtime) which defines abstractions for CUDA methods for allocating memory, initializing and managing streams, and `DeviceContext` which enables users to define and keep track of devices. + +Each curve has its own package which you can find [here](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/curves). If your project uses BN254 you only need to install that single package named [`bn254`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/curves/bn254). + +## Using ICICLE Golang bindings in your project + +To add ICICLE to your `go.mod` file. + +```bash +go get github.com/ingonyama-zk/icicle +``` + +If you want to specify a specific branch + +```bash +go get github.com/ingonyama-zk/icicle@ +``` + +For a specific commit + +```bash +go get github.com/ingonyama-zk/icicle@ +``` + +To build the shared libraries you can run this script: + +``` +./build [G2_enabled] + +curve - The name of the curve to build or "all" to build all curves +G2_enabled - Optional - To build with G2 enabled +``` + +For example if you want to build all curves with G2 enabled you would run: + +```bash +./build.sh all ON +``` + +If you are interested in building a specific curve you would run: + +```bash +./build.sh bls12_381 ON +``` + +Now you can import ICICLE into your project + +```golang +import ( + "github.com/stretchr/testify/assert" + "testing" + + "github.com/ingonyama-zk/icicle/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/wrappers/golang/cuda_runtime" +) +... +``` + +## Running tests + +To run all tests, for all curves: + +```bash +go test --tags=g2 ./... -count=1 +``` + +If you dont want to include g2 tests then drop `--tags=g2`. + +If you wish to run test for a specific curve: + +```bash +go test -count=1 +``` + +## How do Golang bindings work? + +The libraries produced from the CUDA code compilation are used to bind Golang to ICICLE's CUDA code. + +1. These libraries (named `libingo_.a`) can be imported in your Go project to leverage the GPU accelerated functionalities provided by ICICLE. + +2. In your Go project, you can use `cgo` to link these libraries. Here's a basic example on how you can use `cgo` to link these libraries: + +```go +/* +#cgo LDFLAGS: -L/path/to/shared/libs -lingo_bn254 +#include "icicle.h" // make sure you use the correct header file(s) +*/ +import "C" + +func main() { + // Now you can call the C functions from the ICICLE libraries. + // Note that C function calls are prefixed with 'C.' in Go code. +} +``` + +Replace `/path/to/shared/libs` with the actual path where the shared libraries are located on your system. diff --git a/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/msm-pre-computation.md b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/msm-pre-computation.md new file mode 100644 index 000000000..e5f342961 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/msm-pre-computation.md @@ -0,0 +1,92 @@ +# MSM Pre computation + +To understand the theory behind MSM pre computation technique refer to Niall Emmart's [talk](https://youtu.be/KAWlySN7Hm8?feature=shared&t=1734). + +### Supported curves + +`bls12-377`, `bls12-381`, `bn254`, `bw6-761` + +## Core package + +## MSM `PrecomputeBases` + +`PrecomputeBases` and `G2PrecomputeBases` exists for all supported curves. + +#### Description + +This function extends each provided base point $(P)$ with its multiples $(2^lP, 2^{2l}P, ..., 2^{(precompute_factor - 1) \cdot l}P)$, where $(l)$ is a level of precomputation determined by the `precompute_factor`. The extended set of points facilitates faster MSM computations by allowing the MSM algorithm to leverage precomputed multiples of base points, reducing the number of point additions required during the computation. + +The precomputation process is crucial for optimizing MSM operations, especially when dealing with large sets of points and scalars. By precomputing and storing multiples of the base points, the MSM function can more efficiently compute the scalar-point multiplications. + +#### `PrecomputeBases` + +Precomputes bases for MSM by extending each base point with its multiples. + +```go +func PrecomputeBases(points core.HostOrDeviceSlice, precomputeFactor int32, c int32, ctx *cr.DeviceContext, outputBases core.DeviceSlice) cr.CudaError +``` + +##### Parameters + +- **`points`**: A slice of the original affine points to be extended with their multiples. +- **`precomputeFactor`**: Determines the total number of points to precompute for each base point. +- **`c`**: Currently unused; reserved for future compatibility. +- **`ctx`**: CUDA device context specifying the execution environment. +- **`outputBases`**: The device slice allocated for storing the extended bases. + +##### Example + +```go +cfg := GetDefaultMSMConfig() +points := GenerateAffinePoints(1024) +precomputeFactor := 8 +var precomputeOut core.DeviceSlice +_, e := precomputeOut.Malloc(points[0].Size()*points.Len()*int(precomputeFactor), points[0].Size()) + +err := PrecomputeBases(points, precomputeFactor, 0, &cfg.Ctx, precomputeOut) +if err != cr.CudaSuccess { + log.Fatalf("PrecomputeBases failed: %v", err) +} +``` + +#### `G2PrecomputeBases` + +This method is the same as `PrecomputeBases` but for G2 points. Extends each G2 curve base point with its multiples for optimized MSM computations. + +```go +func G2PrecomputeBases(points core.HostOrDeviceSlice, precomputeFactor int32, c int32, ctx *cr.DeviceContext, outputBases core.DeviceSlice) cr.CudaError +``` + +##### Parameters + +- **`points`**: A slice of G2 curve points to be extended. +- **`precomputeFactor`**: The total number of points to precompute for each base. +- **`c`**: Reserved for future use to ensure compatibility with MSM operations. +- **`ctx`**: Specifies the CUDA device context for execution. +- **`outputBases`**: Allocated device slice for the extended bases. + +##### Example + +```go +cfg := G2GetDefaultMSMConfig() +points := G2GenerateAffinePoints(1024) +precomputeFactor := 8 +var precomputeOut core.DeviceSlice +_, e := precomputeOut.Malloc(points[0].Size()*points.Len()*int(precomputeFactor), points[0].Size()) + +err := G2PrecomputeBases(points, precomputeFactor, 0, &cfg.Ctx, precomputeOut) +if err != cr.CudaSuccess { + log.Fatalf("G2PrecomputeBases failed: %v", err) +} +``` + +### Benchmarks + +Benchmarks where performed on a Nvidia RTX 3090Ti. + +| Pre-computation factor | bn254 size `2^20` MSM, ms. | bn254 size `2^12` MSM, size `2^10` batch, ms. | bls12-381 size `2^20` MSM, ms. | bls12-381 size `2^12` MSM, size `2^10` batch, ms. | +| ------------- | ------------- | ------------- | ------------- | ------------- | +| 1 | 14.1 | 82.8 | 25.5 | 136.7 | +| 2 | 11.8 | 76.6 | 20.3 | 123.8 | +| 4 | 10.9 | 73.8 | 18.1 | 117.8 | +| 8 | 10.6 | 73.7 | 17.2 | 116.0 | diff --git a/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/msm.md b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/msm.md new file mode 100644 index 000000000..1f4bd5c60 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/msm.md @@ -0,0 +1,200 @@ +# MSM + + +### Supported curves + +`bls12-377`, `bls12-381`, `bn254`, `bw6-761` + +## MSM Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/wrappers/golang/cuda_runtime" +) + +func Main() { + // Obtain the default MSM configuration. + cfg := GetDefaultMSMConfig() + + // Define the size of the problem, here 2^18. + size := 1 << 18 + + // Generate scalars and points for the MSM operation. + scalars := GenerateScalars(size) + points := GenerateAffinePoints(size) + + // Create a CUDA stream for asynchronous operations. + stream, _ := cr.CreateStream() + var p Projective + + // Allocate memory on the device for the result of the MSM operation. + var out core.DeviceSlice + _, e := out.MallocAsync(p.Size(), p.Size(), stream) + + if e != cr.CudaSuccess { + panic(e) + } + + // Set the CUDA stream in the MSM configuration. + cfg.Ctx.Stream = &stream + cfg.IsAsync = true + + // Perform the MSM operation. + e = Msm(scalars, points, &cfg, out) + + if e != cr.CudaSuccess { + panic(e) + } + + // Allocate host memory for the results and copy the results from the device. + outHost := make(core.HostSlice[Projective], 1) + cr.SynchronizeStream(&stream) + outHost.CopyFromDevice(&out) + + // Free the device memory allocated for the results. + out.Free() +} +``` + +## MSM Method + +```go +func Msm(scalars core.HostOrDeviceSlice, points core.HostOrDeviceSlice, cfg *core.MSMConfig, results core.HostOrDeviceSlice) cr.CudaError +``` + +### Parameters + +- **scalars**: A slice containing the scalars for multiplication. It can reside either in host memory or device memory. +- **points**: A slice containing the points to be multiplied with scalars. Like scalars, these can also be in host or device memory. +- **cfg**: A pointer to an `MSMConfig` object, which contains various configuration options for the MSM operation. +- **results**: A slice where the results of the MSM operation will be stored. This slice can be in host or device memory. + +### Return Value + +- **CudaError**: Returns a CUDA error code indicating the success or failure of the MSM operation. + +## MSMConfig + +The `MSMConfig` structure holds configuration parameters for the MSM operation, allowing customization of its behavior to optimize performance based on the specifics of the operation or the underlying hardware. + +```go +type MSMConfig struct { + Ctx cr.DeviceContext + PrecomputeFactor int32 + C int32 + Bitsize int32 + LargeBucketFactor int32 + batchSize int32 + areScalarsOnDevice bool + AreScalarsMontgomeryForm bool + arePointsOnDevice bool + ArePointsMontgomeryForm bool + areResultsOnDevice bool + IsBigTriangle bool + IsAsync bool +} +``` + +### Fields + +- **Ctx**: Device context containing details like device id and stream. +- **PrecomputeFactor**: Controls the number of extra points to pre-compute. +- **C**: Window bitsize, a key parameter in the "bucket method" for MSM. +- **Bitsize**: Number of bits of the largest scalar. +- **LargeBucketFactor**: Sensitivity to frequently occurring buckets. +- **batchSize**: Number of results to compute in one batch. +- **areScalarsOnDevice**: Indicates if scalars are located on the device. +- **AreScalarsMontgomeryForm**: True if scalars are in Montgomery form. +- **arePointsOnDevice**: Indicates if points are located on the device. +- **ArePointsMontgomeryForm**: True if point coordinates are in Montgomery form. +- **areResultsOnDevice**: Indicates if results are stored on the device. +- **IsBigTriangle**: If `true` MSM will run in Large triangle accumulation if `false` Bucket accumulation will be chosen. Default value: false. +- **IsAsync**: If true, runs MSM asynchronously. + +### Default Configuration + +Use `GetDefaultMSMConfig` to obtain a default configuration, which can then be customized as needed. + +```go +func GetDefaultMSMConfig() MSMConfig +``` + + +## How do I toggle between the supported algorithms? + +When creating your MSM Config you may state which algorithm you wish to use. `cfg.Ctx.IsBigTriangle = true` will activate Large triangle accumulation and `cfg.Ctx.IsBigTriangle = false` will activate Bucket accumulation. + +```go +... + +// Obtain the default MSM configuration. +cfg := GetDefaultMSMConfig() + +cfg.Ctx.IsBigTriangle = true + +... +``` + +## How do I toggle between MSM modes? + +Toggling between MSM modes occurs automatically based on the number of results you are expecting from the `MSM` function. + +The number of results is interpreted from the size of `var out core.DeviceSlice`. Thus its important when allocating memory for `var out core.DeviceSlice` to make sure that you are allocating ` X `. + +```go +... + +batchSize := 3 +var p G2Projective +var out core.DeviceSlice +out.Malloc(batchSize*p.Size(), p.Size()) + +... +``` + +## Support for G2 group + +To activate G2 support first you must make sure you are building the static libraries with G2 feature enabled. + +```bash +./build.sh bls12_381 ON +``` + +Now when importing `icicle`, you should have access to G2 features. + +```go +import ( + "github.com/ingonyama-zk/icicle/wrappers/golang/core" +) +``` + +These features include `G2Projective` and `G2Affine` points as well as a `G2Msm` method. + +```go +... + +cfg := GetDefaultMSMConfig() +size := 1 << 12 +batchSize := 3 +totalSize := size * batchSize +scalars := GenerateScalars(totalSize) +points := G2GenerateAffinePoints(totalSize) + +var p G2Projective +var out core.DeviceSlice +out.Malloc(batchSize*p.Size(), p.Size()) +G2Msm(scalars, points, &cfg, out) + +... +``` + +`G2Msm` works the same way as normal MSM, the difference is that it uses G2 Points. + +Additionally when you are building your application make sure to use the g2 feature flag + +```bash +go build -tags=g2 +``` diff --git a/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/multi-gpu.md b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/multi-gpu.md new file mode 100644 index 000000000..ec00561f1 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/multi-gpu.md @@ -0,0 +1,139 @@ +# Multi GPU APIs + +To learn more about the theory of Multi GPU programming refer to [this part](../multi-gpu.md) of documentation. + +Here we will cover the core multi GPU apis and a [example](#a-multi-gpu-example) + + +## A Multi GPU example + +In this example we will display how you can + +1. Fetch the number of devices installed on a machine +2. For every GPU launch a thread and set an active device per thread. +3. Execute a MSM on each GPU + + +```go +func main() { + numDevices, _ := cuda_runtime.GetDeviceCount() + fmt.Println("There are ", numDevices, " devices available") + wg := sync.WaitGroup{} + + for i := 0; i < numDevices; i++ { + wg.Add(1) + // RunOnDevice makes sure each MSM runs on a single thread + cuda_runtime.RunOnDevice(i, func(args ...any) { + defer wg.Done() + cfg := GetDefaultMSMConfig() + cfg.IsAsync = true + for _, power := range []int{10, 18} { + size := 1 << power // 2^pwr + + // generate random scalars + scalars := GenerateScalars(size) + points := GenerateAffinePoints(size) + + // create a stream and allocate result pointer + stream, _ := cuda_runtime.CreateStream() + var p Projective + var out core.DeviceSlice + _, e := out.MallocAsync(p.Size(), p.Size(), stream) + // assign stream to device context + cfg.Ctx.Stream = &stream + + // execute MSM + e = Msm(scalars, points, &cfg, out) + // read result from device + outHost := make(core.HostSlice[Projective], 1) + outHost.CopyFromDeviceAsync(&out, stream) + out.FreeAsync(stream) + + // sync the stream + cr.SynchronizeStream(&stream) + } + }) + } + wg.Wait() +} +``` + +This example demonstrates a basic pattern for distributing tasks across multiple GPUs. The `RunOnDevice` function ensures that each goroutine is executed on its designated GPU and a corresponding thread. + +## Device Management API + +To streamline device management we offer as part of `cuda_runtime` package methods for dealing with devices. + +### `RunOnDevice` + +Runs a given function on a specific GPU device, ensuring that all CUDA calls within the function are executed on the selected device. + +In Go, most concurrency can be done via Goroutines. However, there is no guarantee that a goroutine stays on a specific host thread. + +`RunOnDevice` was designed to solve this caveat and insure that the goroutine will stay on a specific host thread. + +`RunOnDevice` will lock a goroutine into a specific host thread, sets a current GPU device, runs a provided function, and unlocks the goroutine from the host thread after the provided function finishes. + +While the goroutine is locked to the host thread, the Go runtime will not assign other goroutine's to that host thread. + +**Parameters:** + +- `deviceId int`: The ID of the device on which to run the provided function. Device IDs start from 0. +- `funcToRun func(args ...any)`: The function to be executed on the specified device. +- `args ...any`: Arguments to be passed to `funcToRun`. + +**Behavior:** + +- The function `funcToRun` is executed in a new goroutine that is locked to a specific OS thread to ensure that all CUDA calls within the function target the specified device. +- It's important to note that any goroutines launched within `funcToRun` are not automatically bound to the same GPU device. If necessary, `RunOnDevice` should be called again within such goroutines with the same `deviceId`. + +**Example:** + +```go +RunOnDevice(0, func(args ...any) { + fmt.Println("This runs on GPU 0") + // CUDA-related operations here will target GPU 0 +}, nil) +``` + +### `SetDevice` + +Sets the active device for the current host thread. All subsequent CUDA calls made from this thread will target the specified device. + +**Parameters:** + +- `device int`: The ID of the device to set as the current device. + +**Returns:** + +- `CudaError`: Error code indicating the success or failure of the operation. + +### `GetDeviceCount` + +Retrieves the number of CUDA-capable devices available on the host. + +**Returns:** + +- `(int, CudaError)`: The number of devices and an error code indicating the success or failure of the operation. + +### `GetDevice` + +Gets the ID of the currently active device for the calling host thread. + +**Returns:** + +- `(int, CudaError)`: The ID of the current device and an error code indicating the success or failure of the operation. + +### `GetDeviceFromPointer` + +Retrieves the device associated with a given pointer. + +**Parameters:** + +- `ptr unsafe.Pointer`: Pointer to query. + +**Returns:** + +- `int`: The device ID associated with the memory pointed to by `ptr`. + +This documentation should provide a clear understanding of how to effectively manage multiple GPUs in Go applications using CUDA, with a particular emphasis on the `RunOnDevice` function for executing tasks on specific GPUs. \ No newline at end of file diff --git a/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/ntt.md b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/ntt.md new file mode 100644 index 000000000..135fc0472 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/ntt.md @@ -0,0 +1,104 @@ +# NTT + +### Supported curves + +`bls12-377`, `bls12-381`, `bn254`, `bw6-761` + +## NTT Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/wrappers/golang/cuda_runtime" +) + +func Main() { + // Obtain the default NTT configuration with a predefined coset generator. + cfg := GetDefaultNttConfig() + + // Define the size of the input scalars. + size := 1 << 18 + + // Generate scalars for the NTT operation. + scalars := GenerateScalars(size) + + // Set the direction of the NTT (forward or inverse). + dir := core.KForward + + // Allocate memory for the results of the NTT operation. + results := make(core.HostSlice[ScalarField], size) + + // Perform the NTT operation. + err := Ntt(scalars, dir, &cfg, results) + if err != cr.CudaSuccess { + panic("NTT operation failed") + } +} +``` + +## NTT Method + +```go +func Ntt[T any](scalars core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) core.IcicleError +``` + +### Parameters + +- **scalars**: A slice containing the input scalars for the transform. It can reside either in host memory or device memory. +- **dir**: The direction of the NTT operation (`KForward` or `KInverse`). +- **cfg**: A pointer to an `NTTConfig` object, containing configuration options for the NTT operation. +- **results**: A slice where the results of the NTT operation will be stored. This slice can be in host or device memory. + +### Return Value + +- **CudaError**: Returns a CUDA error code indicating the success or failure of the NTT operation. + +## NTT Configuration (NTTConfig) + +The `NTTConfig` structure holds configuration parameters for the NTT operation, allowing customization of its behavior to optimize performance based on the specifics of your protocol. + +```go +type NTTConfig[T any] struct { + Ctx cr.DeviceContext + CosetGen T + BatchSize int32 + ColumnsBatch bool + Ordering Ordering + areInputsOnDevice bool + areOutputsOnDevice bool + IsAsync bool + NttAlgorithm NttAlgorithm +} +``` + +### Fields + +- **Ctx**: Device context containing details like device ID and stream ID. +- **CosetGen**: Coset generator used for coset (i)NTTs, defaulting to no coset being used. +- **BatchSize**: The number of NTTs to compute in one operation, defaulting to 1. +- **ColumnsBatch**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. Defaults to `false`. +- **Ordering**: Ordering of inputs and outputs (`KNN`, `KNR`, `KRN`, `KRR`, `KMN`, `KNM`), affecting how data is arranged. +- **areInputsOnDevice**: Indicates if input scalars are located on the device. +- **areOutputsOnDevice**: Indicates if results are stored on the device. +- **IsAsync**: Controls whether the NTT operation runs asynchronously. +- **NttAlgorithm**: Explicitly select the NTT algorithm. Default value: Auto (the implementation selects radix-2 or mixed-radix algorithm based on heuristics). + +### Default Configuration + +Use `GetDefaultNTTConfig` to obtain a default configuration, customizable as needed. + +```go +func GetDefaultNTTConfig[T any](cosetGen T) NTTConfig[T] +``` + +### Initializing the NTT Domain + +Before performing NTT operations, it's necessary to initialize the NTT domain; it only needs to be called once per GPU since the twiddles are cached. + +```go +func InitDomain(primitiveRoot ScalarField, ctx cr.DeviceContext, fastTwiddles bool) core.IcicleError +``` + +This function initializes the domain with a given primitive root, optionally using fast twiddle factors to optimize the computation. diff --git a/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/vec-ops.md b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/vec-ops.md new file mode 100644 index 000000000..69dbe5b9e --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/golang-bindings/vec-ops.md @@ -0,0 +1,132 @@ +# Vector Operations + +## Overview + +The VecOps API provides efficient vector operations such as addition, subtraction, and multiplication. + +## Example + +### Vector addition + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/wrappers/golang/cuda_runtime" +) + +func main() { + testSize := 1 << 12 + a := GenerateScalars(testSize) + b := GenerateScalars(testSize) + out := make(core.HostSlice[ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector addition + err := VecOp(a, b, out, cfg, core.Add) + if err != cr.CudaSuccess { + panic("Vector addition failed") + } +} +``` + +### Vector Subtraction + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/wrappers/golang/cuda_runtime" +) + +func main() { + testSize := 1 << 12 + a := GenerateScalars(testSize) + b := GenerateScalars(testSize) + out := make(core.HostSlice[ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector subtraction + err := VecOp(a, b, out, cfg, core.Sub) + if err != cr.CudaSuccess { + panic("Vector subtraction failed") + } +} +``` + +### Vector Multiplication + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/wrappers/golang/cuda_runtime" +) + +func main() { + testSize := 1 << 12 + a := GenerateScalars(testSize) + b := GenerateScalars(testSize) + out := make(core.HostSlice[ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector multiplication + err := VecOp(a, b, out, cfg, core.Mul) + if err != cr.CudaSuccess { + panic("Vector multiplication failed") + } +} +``` + +## VecOps Method + +```go +func VecOp(a, b, out core.HostOrDeviceSlice, config core.VecOpsConfig, op core.VecOps) (ret cr.CudaError) +``` + +### Parameters + +- **a**: The first input vector. +- **b**: The second input vector. +- **out**: The output vector where the result of the operation will be stored. +- **config**: A `VecOpsConfig` object containing various configuration options for the vector operations. +- **op**: The operation to perform, specified as one of the constants (`Sub`, `Add`, `Mul`) from the `VecOps` type. + +### Return Value + +- **CudaError**: Returns a CUDA error code indicating the success or failure of the vector operation. + +## VecOpsConfig + +The `VecOpsConfig` structure holds configuration parameters for the vector operations, allowing customization of its behavior. + +```go +type VecOpsConfig struct { + Ctx cr.DeviceContext + isAOnDevice bool + isBOnDevice bool + isResultOnDevice bool + IsResultMontgomeryForm bool + IsAsync bool +} +``` + +### Fields + +- **Ctx**: Device context containing details like device ID and stream ID. +- **isAOnDevice**: Indicates if vector `a` is located on the device. +- **isBOnDevice**: Indicates if vector `b` is located on the device. +- **isResultOnDevice**: Specifies where the result vector should be stored (device or host memory). +- **IsResultMontgomeryForm**: Determines if the result vector should be in Montgomery form. +- **IsAsync**: Controls whether the vector operation runs asynchronously. + +### Default Configuration + +Use `DefaultVecOpsConfig` to obtain a default configuration, customizable as needed. + +```go +func DefaultVecOpsConfig() VecOpsConfig +``` diff --git a/docs/versioned_docs/version-1.10.1/icicle/integrations.md b/docs/versioned_docs/version-1.10.1/icicle/integrations.md new file mode 100644 index 000000000..4def46015 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/integrations.md @@ -0,0 +1,97 @@ +# ICICLE integrated provers + +ICICLE has been used by companies and projects such as [Celer Network](https://github.com/celer-network), [Consensys Gnark](https://github.com/Consensys/gnark), [EZKL](https://blog.ezkl.xyz/post/acceleration/) and others to accelerate their ZK proving pipeline. + +Many of these integrations have been a collaboration between Ingonyama and the integrating company. We have learned a lot about designing GPU based ZK provers. + +If you're interested in understanding these integrations better or learning how you can use ICICLE to accelerate your existing ZK proving pipeline this is the place for you. + +## A primer to building your own integrations + +Lets illustrate an ICICLE integration, so you can understand the core API and design overview of ICICLE. + +![ICICLE architecture](/img/architecture-high-level.png) + +Engineers usually use a cryptographic library to implement their ZK protocols. These libraries implement efficient primitives which are used as building blocks for the protocol; ICICLE is such a library. The difference is that ICICLE is designed from the start to run on GPUs; the Rust and Golang APIs abstract away all low level CUDA details. Our goal was to allow developers with no GPU experience to quickly get started with ICICLE. + +A developer may use ICICLE with two main approaches in mind. + +1. Drop-in replacement approach. +2. End-to-End GPU replacement approach. + +The first approach for GPU-accelerating your Prover with ICICLE is quick to implement, but it has limitations, such as reduced memory optimization and limited protocol tuning for GPUs. It's a solid starting point, but those committed to fully leveraging GPU acceleration should consider a more comprehensive approach. + +A End-to-End GPU replacement means performing the entire ZK proof on the GPU. This approach will reduce latency to a minimum and requires you to change the way you implement the protocol to be more GPU friendly. This approach will take full advantage of GPU acceleration. Redesigning your prover this way may take more engineering effort but we promise you that its worth it! + +## Using ICICLE integrated provers + +Here we cover how a developer can run existing circuits on ICICLE integrated provers. + +### Gnark + +[Gnark](https://github.com/Consensys/gnark) officially supports GPU proving with ICICLE. Currently only Groth16 on curve `BN254` is supported. This means that if you are currently using Gnark to write your circuits you can enjoy GPU acceleration without making many changes. + +:::info + +Currently ICICLE has been merged to Gnark [master branch](https://github.com/Consensys/gnark), however the [latest release](https://github.com/Consensys/gnark/releases/tag/v0.9.1) is from October 2023. + +::: + +Make sure your golang circuit project has `gnark` as a dependency and that you are using the master branch for now. + +``` +go get github.com/consensys/gnark@master +``` + +You should see two indirect dependencies added. + +``` +... + github.com/ingonyama-zk/icicle v0.1.0 // indirect + github.com/ingonyama-zk/iciclegnark v0.1.1 // indirect +... +``` + +:::info +As you may notice we are using ICICLE v0.1 here since golang bindings are only support in ICICLE v0.1 for the time being. +::: + +To switch over to ICICLE proving, make sure to change the backend you are using, below is an example of how this should be done. + +``` +// toggle on +proofIci, err := groth16.Prove(ccs, pk, secretWitness, backend.WithIcicleAcceleration()) + +// toggle off +proof, err := groth16.Prove(ccs, pk, secretWitness) +``` + +Now that you have enabled `WithIcicleAcceleration` backend simple change the way your run your circuits to: + +``` +go run -tags=icicle main.go +``` + +Your logs should look something like this if everything went as expected. + +``` +13:12:05 INF compiling circuit +13:12:05 INF parsed circuit inputs nbPublic=1 nbSecret=1 +13:12:05 INF building constraint builder nbConstraints=3 +13:12:05 DBG precomputing proving key in GPU acceleration=icicle backend=groth16 curve=bn254 nbConstraints=3 +13:12:05 DBG constraint system solver done nbConstraints=3 took=0.070259 +13:12:05 DBG prover done acceleration=icicle backend=groth16 curve=bn254 nbConstraints=3 took=80.356684 +13:12:05 DBG verifier done backend=groth16 curve=bn254 took=1.843888 +``` + +`acceleration=icicle` indicates that the prover is running in acceleration mode with ICICLE. + +You can reference the [Gnark docs](https://github.com/Consensys/gnark?tab=readme-ov-file#gpu-support) for further information. + +### Halo2 + +[Halo2](https://github.com/zkonduit/halo2) fork integrated with ICICLE for GPU acceleration. This means that you can run your existing Halo2 circuits with GPU acceleration just by activating a feature flag. + +To enable GPU acceleration just enable `icicle_gpu` [feature flag](https://github.com/zkonduit/halo2/blob/3d7b5e61b3052680ccb279e05bdcc21dd8a8fedf/halo2_proofs/Cargo.toml#L102). + +This feature flag will seamlessly toggle on GPU acceleration for you. diff --git a/docs/versioned_docs/version-1.10.1/icicle/introduction.md b/docs/versioned_docs/version-1.10.1/icicle/introduction.md new file mode 100644 index 000000000..2c4ac8f05 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/introduction.md @@ -0,0 +1,260 @@ +# Getting started with ICICLE + +This guide is oriented towards developers who want to start writing code with the ICICLE libraries. If you just want to run your existing ZK circuits on GPU refer to [this guide](./integrations.md#using-icicle-integrated-provers) please. + +## ICICLE repository overview + +![ICICLE API overview](/img/apilevels.png) + +The diagram above displays the general architecture of ICICLE and the API layers that exist. The CUDA API, which we also call ICICLE Core, is the lowest level and is comprised of CUDA kernels which implement all primitives such as MSM as well as C++ wrappers which expose these methods for different curves. + +ICICLE Core compiles into a static library. This library can be used with our official Golang and Rust wrappers or you can implement a wrapper for it in any language. + +Based on this dependency architecture, the ICICLE repository has three main sections, each of which is independent from the other. + +- ICICLE core +- ICICLE Rust bindings +- ICICLE Golang bindings + +### ICICLE Core + +[ICICLE core](https://github.com/ingonyama-zk/icicle/tree/main/icicle) contains all the low level CUDA code implementing primitives such as [points](https://github.com/ingonyama-zk/icicle/tree/main/icicle/primitives) and [MSM](https://github.com/ingonyama-zk/icicle/tree/main/icicle/appUtils/msm). There also exists higher level C++ wrappers to expose the low level CUDA primitives ([example](https://github.com/ingonyama-zk/icicle/blob/c1a32a9879a7612916e05aa3098f76144de4109e/icicle/appUtils/msm/msm.cu#L1)). + +ICICLE Core would typically be compiled into a static library and used in a third party language such as Rust or Golang. + +### ICICLE Rust and Golang bindings + +- [ICICLE Rust bindings](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust) +- [ICICLE Golang bindings](https://github.com/ingonyama-zk/icicle/tree/main/goicicle) + +These bindings allow you to easily use ICICLE in a Rust or Golang project. Setting up Golang bindings requires a bit of extra steps compared to the Rust bindings which utilize the `cargo build` tool. + +## Running ICICLE + +This guide assumes that you have a Linux or Windows machine with an Nvidia GPU installed. If you don't have access to an Nvidia GPU you can access one for free on [Google Colab](https://colab.google/). + +### Prerequisites + +- NVCC (version 12.0 or newer) +- cmake 3.18 and above +- GCC - version 9 or newer is recommended. +- Any Nvidia GPU +- Linux or Windows operating system. + +#### Optional Prerequisites + +- Docker, latest version. +- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/index.html) + +If you don't wish to install these prerequisites you can follow this tutorial using a [ZK-Container](https://github.com/ingonyama-zk/icicle/blob/main/Dockerfile) (docker container). To learn more about using ZK-Containers [read this](../ZKContainers.md). + +### Setting up ICICLE and running tests + +The objective of this guide is to make sure you can run the ICICLE Core, Rust and Golang tests. Achieving this will ensure you know how to setup ICICLE and run a ICICLE program. For simplicity, we will be using the ICICLE docker container as our environment, however, you may install the prerequisites on your machine and follow the same commands in your terminal. + +#### Setting up our environment + +Lets begin by cloning the ICICLE repository: + +```sh +git clone https://github.com/ingonyama-zk/icicle +``` + +We will proceed to build the docker image [found here](https://github.com/ingonyama-zk/icicle/blob/main/Dockerfile): + +```sh +docker build -t icicle-demo . +docker run -it --runtime=nvidia --gpus all --name icicle_container icicle-demo +``` + +- `-it` runs the container in interactive mode with a terminal. +- `--gpus all` Allocate all available GPUs to the container. You can also specify which GPUs to use if you don't want to allocate all. +- `--runtime=nvidia` Use the NVIDIA runtime, necessary for GPU support. + +To read more about these settings reference this [article](https://developer.nvidia.com/nvidia-container-runtime). + +If you accidentally close your terminal and want to reconnect just call: + +```sh +docker exec -it icicle_container bash +``` + +Lets make sure that we have the correct CUDA version before proceeding + +```sh +nvcc --version +``` + +You should see something like this + +```sh +nvcc: NVIDIA (R) Cuda compiler driver +Copyright (c) 2005-2023 NVIDIA Corporation +Built on Tue_Aug_15_22:02:13_PDT_2023 +Cuda compilation tools, release 12.2, V12.2.140 +Build cuda_12.2.r12.2/compiler.33191640_0 +``` + +Make sure the release version is at least 12.0. + +#### ICICLE Core + +ICICLE Core is found under [`/icicle`](https://github.com/ingonyama-zk/icicle/tree/main/icicle). To build and run the tests first: + +```sh +cd icicle +``` + +We are going to compile ICICLE for a specific curve + +```sh +mkdir -p build +cmake -S . -B build -DCURVE=bn254 -DBUILD_TESTS=ON +cmake --build build +``` + +`-DBUILD_TESTS=ON` compiles the tests, without this flag `ctest` won't work. +`-DCURVE=bn254` tells the compiler which curve to build. You can find a list of supported curves [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/curves). + +The output in `build` folder should include the static libraries for the compiled curve. + +:::info + +Make sure to only use `-DBUILD_TESTS=ON` for running tests as the archive output will only be available when `-DBUILD_TESTS=ON` is not supplied. + +::: + +To run the test + +```sh +cd build +ctest +``` + +#### ICICLE Rust + +The rust bindings work by first compiling the CUDA static libraries as seen [here](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-curves/icicle-bn254/build.rs). The compilation of CUDA and the Rust library is all handled by the rust build toolchain. + +Similar to ICICLE Core here we also have to compile per curve. + +Lets compile curve `bn254` + +```sh +cd wrappers/rust/icicle-curves/icicle-bn254 +``` + +Now lets build our library + +```sh +cargo build --release +``` + +This may take a couple of minutes since we are compiling both the CUDA and Rust code. + +To run the tests + +```sh +cargo test +``` + +We also include some benchmarks + +```sh +cargo bench +``` + +#### ICICLE Golang + +Golang is WIP in v1, coming soon. Please checkout a previous [release v0.1.0](https://github.com/ingonyama-zk/icicle/releases/tag/v0.1.0) for golang bindings. + +### Running ICICLE examples + +ICICLE examples can be found [here](https://github.com/ingonyama-zk/icicle-examples) these examples cover some simple use cases using C++, rust and golang. + +In each example directory, ZK-container files are located in a subdirectory `.devcontainer`. + +```sh +msm/ +├── .devcontainer + ├── devcontainer.json + └── Dockerfile +``` + +Lets run one of our C++ examples, in this case the [MSM example](https://github.com/ingonyama-zk/icicle-examples/blob/main/c%2B%2B/msm/example.cu). + +Clone the repository + +```sh +git clone https://github.com/ingonyama-zk/icicle-examples.git +cd icicle-examples +``` + +Enter the test directory + +```sh +cd c++/msm +``` + +Now lets build our docker file and run the test inside it. Make sure you have installed the [optional prerequisites](#optional-prerequisites). + +```sh +docker build -t icicle-example-msm -f .devcontainer/Dockerfile . +``` + +Lets start and enter the container + +```sh +docker run -it --rm --gpus all -v .:/icicle-example icicle-example-msm +``` + +to run the example + +```sh +rm -rf build +mkdir -p build +cmake -S . -B build +cmake --build build +./build/example +``` + +You can now experiment with our other examples, perhaps try to run a rust or golang example next. + +## Writing new bindings for ICICLE + +Since ICICLE Core is written in CUDA / C++ its really simple to generate static libraries. These static libraries can be installed on any system and called by higher level languages such as Golang. + +static libraries can be loaded into memory once and used by multiple programs, reducing memory usage and potentially improving performance. They also allow you to separate functionality into distinct modules so your static library may need to compile only specific features that you want to use. + +Lets review the Golang bindings since its a pretty verbose example (compared to rust which hides it pretty well) of using static libraries. Golang has a library named `CGO` which can be used to link static libraries. Here's a basic example on how you can use cgo to link these libraries: + +```go +/* +#cgo LDFLAGS: -L/path/to/shared/libs -lbn254 -lbls12_381 -lbls12_377 -lbw6_671 +#include "icicle.h" // make sure you use the correct header file(s) +*/ +import "C" + +func main() { + // Now you can call the C functions from the ICICLE libraries. + // Note that C function calls are prefixed with 'C.' in Go code. + + out := (*C.BN254_projective_t)(unsafe.Pointer(p)) + in := (*C.BN254_affine_t)(unsafe.Pointer(affine)) + + C.projective_from_affine_bn254(out, in) +} +``` + +The comments on the first line tell `CGO` which libraries to import as well as which header files to include. You can then call methods which are part of the static library and defined in the header file, `C.projective_from_affine_bn254` is an example. + +If you wish to create your own bindings for a language of your choice we suggest you start by investigating how you can call static libraries. + +### ICICLE Adapters + +One of the core ideas behind ICICLE is that developers can gradually accelerate their provers. Many protocols are written using other cryptographic libraries and completely replacing them may be complex and time consuming. + +Therefore we offer adapters for various popular libraries, these adapters allow us to convert points and scalars between different formats defined by various libraries. Here is a list: + +Golang adapters: + +- [Gnark crypto adapter](https://github.com/ingonyama-zk/iciclegnark) diff --git a/docs/versioned_docs/version-1.10.1/icicle/multi-gpu.md b/docs/versioned_docs/version-1.10.1/icicle/multi-gpu.md new file mode 100644 index 000000000..a57e33262 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/multi-gpu.md @@ -0,0 +1,64 @@ +# Multi GPU with ICICLE + +:::info + +If you are looking for the Multi GPU API documentation refer here for [Rust](./rust-bindings/multi-gpu.md). + +::: + +One common challenge with Zero-Knowledge computation is managing the large input sizes. It's not uncommon to encounter circuits surpassing 2^25 constraints, pushing the capabilities of even advanced GPUs to their limits. To effectively scale and process such large circuits, leveraging multiple GPUs in tandem becomes a necessity. + +Multi-GPU programming involves developing software to operate across multiple GPU devices. Lets first explore different approaches to Multi-GPU programming then we will cover how ICICLE allows you to easily develop youR ZK computations to run across many GPUs. + + +## Approaches to Multi GPU programming + +There are many [different strategies](https://github.com/NVIDIA/multi-gpu-programming-models) available for implementing multi GPU, however, it can be split into two categories. + +### GPU Server approach + +This approach usually involves a single or multiple CPUs opening threads to read / write from multiple GPUs. You can think about it as a scaled up HOST - Device model. + +![Multi-GPU Server Approach](/img/image.png) + +This approach won't let us tackle larger computation sizes but it will allow us to compute multiple computations which we wouldn't be able to load onto a single GPU. + +For example let's say that you had to compute two MSMs of size 2^26 on a 16GB VRAM GPU you would normally have to perform them asynchronously. However, if you double the number of GPUs in your system you can now run them in parallel. + + +### Inter GPU approach + +This approach involves a more sophisticated approach to multi GPU computation. Using technologies such as [GPUDirect, NCCL, NVSHMEM](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-cwes1084/) and NVLink it's possible to combine multiple GPUs and split a computation among different devices. + +This approach requires redesigning the algorithm at the software level to be compatible with splitting amongst devices. In some cases, to lower latency to a minimum, special inter GPU connections would be installed on a server to allow direct communication between multiple GPUs. + + +# Writing ICICLE Code for Multi GPUs + +The approach we have taken for the moment is a GPU Server approach; we assume you have a machine with multiple GPUs and you wish to run some computation on each GPU. + +To dive deeper and learn about the API check out the docs for our different ICICLE API + +- [Rust Multi GPU APIs](./rust-bindings/multi-gpu.md) +- C++ Multi GPU APIs + + +## Best practices + +- Never hardcode device IDs, if you want your software to take advantage of all GPUs on a machine use methods such as `get_device_count` to support arbitrary number of GPUs. + +- Launch one CPU thread per GPU. To avoid [nasty errors](https://developer.nvidia.com/blog/cuda-pro-tip-always-set-current-device-avoid-multithreading-bugs/) and hard to read code we suggest that for every GPU you create a dedicated thread. Within a CPU thread you should be able to launch as many tasks as you wish for a GPU as long as they all run on the same GPU id. This will make your code way more manageable, easy to read and performant. + +## ZKContainer support for multi GPUs + +Multi GPU support should work with ZK-Containers by simply defining which devices the docker container should interact with: + +```sh +docker run -it --gpus '"device=0,2"' zk-container-image +``` + +If you wish to expose all GPUs + +```sh +docker run --gpus all zk-container-image +``` diff --git a/docs/versioned_docs/version-1.10.1/icicle/overview.md b/docs/versioned_docs/version-1.10.1/icicle/overview.md new file mode 100644 index 000000000..4ee3909f5 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/overview.md @@ -0,0 +1,58 @@ +# What is ICICLE? + +[ICICLE](https://github.com/ingonyama-zk/icicle) is a cryptography library for ZK using GPUs. ICICLE implements blazing fast cryptographic primitives such as EC operations, MSM, NTT, Poseidon hash and more on GPU. + +ICICLE allows developers with minimal GPU experience to effortlessly accelerate their ZK application; from our experiments, even the most naive implementation may yield 10X improvement in proving times. + +ICICLE has been used by many leading ZK companies such as [Celer Network](https://github.com/celer-network), [Gnark](https://github.com/Consensys/gnark) and others to accelerate their ZK proving pipeline. + +## Dont have access to a GPU? + +We understand that not all developers have access to a GPU and we don't want this to limit anyone from developing with ICICLE. +Here are some ways we can help you gain access to GPUs: + +### Grants + +At Ingonyama we are interested in accelerating the progress of ZK and cryptography. If you are an engineer, developer or an academic researcher we invite you to checkout [our grant program](https://www.ingonyama.com/blog/icicle-for-researchers-grants-challenges). We will give you access to GPUs and even pay you to do your dream research! + +### Google Colab + +This is a great way to get started with ICICLE instantly. Google Colab offers free GPU access to a NVIDIA T4 instance, it's acquired with 16 GB of memory which should be enough for experimenting and even prototyping with ICICLE. + +For an extensive guide on how to setup Google Colab with ICICLE refer to [this article](./colab-instructions.md). + +If none of these options are appropriate for you reach out to us on [telegram](https://t.me/RealElan) we will do our best to help you. + +### Vast.ai + +[Vast.ai](https://vast.ai/) is a global GPU marketplace where you can rent many different types of GPUs by the hour for [competitive pricing](https://vast.ai/pricing). They provide on-demand and interruptible rentals depending on your need or use case; you can learn more about their rental types [here](https://vast.ai/faq#rental-types). + +:::note + +If none of these options suit your needs, contact us on [telegram](https://t.me/RealElan) for assistance. We're committed to ensuring that a lack of a GPU doesn't become a bottleneck for you. If you need help with setup or any other issues, we're here to do our best to help you. + +::: + +## What can you do with ICICLE? + +[ICICLE](https://github.com/ingonyama-zk/icicle) can be used in the same way you would use any other cryptography library. While developing and integrating ICICLE into many proof systems, we found some use case categories: + +### Circuit developers + +If you are a circuit developer and are experiencing bottlenecks while running your circuits, an ICICLE integrated prover may be the solution. + +ICICLE has been integrated into a number of popular ZK provers including [Gnark prover](https://github.com/Consensys/gnark) and [Halo2](https://github.com/zkonduit/halo2). This means that you can enjoy GPU acceleration for your existing circuits immediately without writing a single line of code by simply switching on the GPU prover flag! + +### Integrating into existing ZK provers + +From our collaborations we have learned that its possible to accelerate a specific part of your prover to solve for a specific bottleneck. + +ICICLE can be used to accelerate specific parts of your prover without completely rewriting your ZK prover. + +### Developing your own ZK provers + +If your goal is to build a ZK prover from the ground up, ICICLE is an ideal tool for creating a highly optimized and scalable ZK prover. A key benefit of using GPUs with ICICLE is the ability to scale your ZK prover efficiently across multiple machines within a data center. + +### Developing proof of concepts + +ICICLE is also ideal for developing small prototypes. ICICLE has Golang and Rust bindings so you can easily develop a library implementing a specific primitive using ICICLE. An example would be develop a KZG commitment library using ICICLE. diff --git a/docs/versioned_docs/version-1.10.1/icicle/primitives/image-1.png b/docs/versioned_docs/version-1.10.1/icicle/primitives/image-1.png new file mode 100644 index 000000000..cc0ba5916 Binary files /dev/null and b/docs/versioned_docs/version-1.10.1/icicle/primitives/image-1.png differ diff --git a/docs/versioned_docs/version-1.10.1/icicle/primitives/image-2.png b/docs/versioned_docs/version-1.10.1/icicle/primitives/image-2.png new file mode 100644 index 000000000..e59a16925 Binary files /dev/null and b/docs/versioned_docs/version-1.10.1/icicle/primitives/image-2.png differ diff --git a/docs/versioned_docs/version-1.10.1/icicle/primitives/image-3.png b/docs/versioned_docs/version-1.10.1/icicle/primitives/image-3.png new file mode 100644 index 000000000..75d3b4329 Binary files /dev/null and b/docs/versioned_docs/version-1.10.1/icicle/primitives/image-3.png differ diff --git a/docs/versioned_docs/version-1.10.1/icicle/primitives/image.png b/docs/versioned_docs/version-1.10.1/icicle/primitives/image.png new file mode 100644 index 000000000..0fa4826e9 Binary files /dev/null and b/docs/versioned_docs/version-1.10.1/icicle/primitives/image.png differ diff --git a/docs/versioned_docs/version-1.10.1/icicle/primitives/msm.md b/docs/versioned_docs/version-1.10.1/icicle/primitives/msm.md new file mode 100644 index 000000000..81e5ce6f0 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/primitives/msm.md @@ -0,0 +1,96 @@ +# MSM - Multi scalar multiplication + +MSM stands for Multi scalar multiplication, its defined as: + + + M + S + M + ( + a + , + G + ) + = + + ∑ + + j + = + 0 + + + n + − + 1 + + + + a + j + + + G + j + + + +Where + +$G_j \in G$ - points from an Elliptic Curve group. + +$a_0, \ldots, a_n$ - Scalars + +$MSM(a, G) \in G$ - a single EC (elliptic curve) point + +In words, MSM is the sum of scalar and EC point multiplications. We can see from this definition that the core operations occurring are Modular Multiplication and Elliptic curve point addition. Its obvious that multiplication can be computed in parallel and then the products summed, making MSM inherently parallelizable. + +Accelerating MSM is crucial to a ZK protocol's performance due to the [large percent of run time](https://hackmd.io/@0xMonia/SkQ6-oRz3#Hardware-acceleration-in-action) they take when generating proofs. + +You can learn more about how MSMs work from this [video](https://www.youtube.com/watch?v=Bl5mQA7UL2I) and from our resource list on [Ingopedia](https://www.ingonyama.com/ingopedia/msm). + +## Supported curves + +MSM supports the following curves: + +`bls12-377`, `bls12-381`, `bn254`, `bw6-761`, `grumpkin` + + +## Supported Bindings + +- [Golang](../golang-bindings/msm.md) +- [Rust](../rust-bindings//msm.md) + +## Supported algorithms + +Our MSM implementation supports two algorithms `Bucket accumulation` and `Large triangle accumulation`. + +### Bucket accumulation + +The Bucket Accumulation algorithm is a method of dividing the overall MSM task into smaller, more manageable sub-tasks. It involves partitioning scalars and their corresponding points into different "buckets" based on the scalar values. + +Bucket Accumulation can be more parallel-friendly because it involves dividing the computation into smaller, independent tasks, distributing scalar-point pairs into buckets and summing points within each bucket. This division makes it well suited for parallel processing on GPUs. + +#### When should I use Bucket accumulation? + +In scenarios involving large MSM computations with many scalar-point pairs, the ability to parallelize operations makes Bucket Accumulation more efficient. The larger the MSM task, the more significant the potential gains from parallelization. + +### Large triangle accumulation + +Large Triangle Accumulation is a method for optimizing MSM which focuses on reducing the number of point doublings in the computation. This algorithm is based on the observation that the number of point doublings can be minimized by structuring the computation in a specific manner. + +#### When should I use Large triangle accumulation? + +The Large Triangle Accumulation algorithm is more sequential in nature, as it builds upon each step sequentially (accumulating sums and then performing doubling). This structure can make it less suitable for parallelization but potentially more efficient for a large batch of smaller MSM computations. + +## MSM Modes + +ICICLE MSM also supports two different modes `Batch MSM` and `Single MSM` + +Batch MSM allows you to run many MSMs with a single API call, Single MSM will launch a single MSM computation. + +### Which mode should I use? + +This decision is highly dependent on your use case and design. However, if your design allows for it, using batch mode can significantly improve efficiency. Batch processing allows you to perform multiple MSMs leveraging the parallel processing capabilities of GPUs. + +Single MSM mode should be used when batching isn't possible or when you have to run a single MSM. diff --git a/docs/versioned_docs/version-1.10.1/icicle/primitives/ntt.md b/docs/versioned_docs/version-1.10.1/icicle/primitives/ntt.md new file mode 100644 index 000000000..bac22944f --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/primitives/ntt.md @@ -0,0 +1,159 @@ +# NTT - Number Theoretic Transform + +The Number Theoretic Transform (NTT) is a variant of the Fourier Transform used over finite fields, particularly those of integers modulo a prime number. NTT operates in a discrete domain and is used primarily in applications requiring modular arithmetic, such as cryptography and polynomial multiplication. + +NTT is defined similarly to the Discrete Fourier Transform (DFT), but instead of using complex roots of unity, it uses roots of unity within a finite field. The definition hinges on the properties of the finite field, specifically the existence of a primitive root of unity of order $N$ (where $N$ is typically a power of 2), and the modulo operation is performed with respect to a specific prime number that supports these roots. + +Formally, given a sequence of integers $a_0, a_1, ..., a_{N-1}$, the NTT of this sequence is another sequence of integers $A_0, A_1, ..., A_{N-1}$, computed as follows: + +$$ +A_k = \sum_{n=0}^{N-1} a_n \cdot \omega^{nk} \mod p +$$ + +where: +- $N$ is the size of the input sequence and is a power of 2, +- $p$ is a prime number such that $p = kN + 1$ for some integer $k$, ensuring that $p$ supports the existence of $N$th roots of unity, +- $\omega$ is a primitive $N$th root of unity modulo $p$, meaning $\omega^N \equiv 1 \mod p$ and no smaller positive power of $\omega$ is congruent to 1 modulo $p$, +- $k$ ranges from 0 to $N-1$, and it indexes the output sequence. + +The NTT is particularly useful because it enables efficient polynomial multiplication under modulo arithmetic, crucial for algorithms in cryptographic protocols, and other areas requiring fast modular arithmetic operations. + +There exists also INTT which is the inverse operation of NTT. INTT can take as input an output sequence of integers from an NTT and reconstruct the original sequence. + +# Using NTT + +### Supported curves + +NTT supports the following curves: + +`bls12-377`, `bls12-381`, `bn-254`, `bw6-761` + +## Supported Bindings + +- [Golang](../golang-bindings/ntt.md) +- [Rust](../rust-bindings/ntt.md) + +### Examples + +- [Rust API examples](https://github.com/ingonyama-zk/icicle/blob/d84ffd2679a4cb8f8d1ac2ad2897bc0b95f4eeeb/examples/rust/ntt/src/main.rs#L1) + +- [C++ API examples](https://github.com/ingonyama-zk/icicle/blob/d84ffd2679a4cb8f8d1ac2ad2897bc0b95f4eeeb/examples/c%2B%2B/ntt/example.cu#L1) + +### Ordering + +The `Ordering` enum defines how inputs and outputs are arranged for the NTT operation, offering flexibility in handling data according to different algorithmic needs or compatibility requirements. It primarily affects the sequencing of data points for the transform, which can influence both performance and the compatibility with certain algorithmic approaches. The available ordering options are: + +- **`kNN` (Natural-Natural):** Both inputs and outputs are in their natural order. This is the simplest form of ordering, where data is processed in the sequence it is given, without any rearrangement. + +- **`kNR` (Natural-Reversed):** Inputs are in natural order, while outputs are in bit-reversed order. This ordering is typically used in algorithms that benefit from having the output in a bit-reversed pattern. + +- **`kRN` (Reversed-Natural):** Inputs are in bit-reversed order, and outputs are in natural order. This is often used with the Cooley-Tukey FFT algorithm. + +- **`kRR` (Reversed-Reversed):** Both inputs and outputs are in bit-reversed order. + +- **`kNM` (Natural-Mixed):** Inputs are provided in their natural order, while outputs are arranged in a digit-reversed (mixed) order. This ordering is good for mixed radix NTT operations, where the mixed or digit-reversed ordering of outputs is a generalization of the bit-reversal pattern seen in simpler, radix-2 cases. + +- **`kMN` (Mixed-Natural):** Inputs are in a digit-reversed (mixed) order, while outputs are restored to their natural order. This ordering would primarily be used for mixed radix NTT + +Choosing an algorithm is heavily dependent on your use case. For example Cooley-Tukey will often use `kRN` and Gentleman-Sande often uses `kNR`. + +### Modes + +NTT also supports two different modes `Batch NTT` and `Single NTT` + +Batch NTT allows you to run many NTTs with a single API call, Single MSM will launch a single MSM computation. + +Deciding weather to use `batch NTT` vs `single NTT` is highly dependent on your application and use case. + +**Single NTT Mode** + +- Choose this mode when your application requires processing individual NTT operations in isolation. + +**Batch NTT Mode** + +- Batch NTT mode can significantly reduce read/write as well as computation overhead by executing multiple NTT operations in parallel. + +- Batch mode may also offer better utilization of computational resources (memory and compute). + +## Supported algorithms + +Our NTT implementation supports two algorithms `radix-2` and `mixed-radix`. + +### Radix 2 + +At its core, the Radix-2 NTT algorithm divides the problem into smaller sub-problems, leveraging the properties of "divide and conquer" to reduce the overall computational complexity. The algorithm operates on sequences whose lengths are powers of two. + +1. **Input Preparation:** + The input is a sequence of integers $a_0, a_1, \ldots, a_{N-1}, \text{ where } N$ is a power of two. + +2. **Recursive Decomposition:** + The algorithm recursively divides the input sequence into smaller sequences. At each step, it separates the sequence into even-indexed and odd-indexed elements, forming two subsequences that are then processed independently. + +3. **Butterfly Operations:** + The core computational element of the Radix-2 NTT is the "butterfly" operation, which combines pairs of elements from the sequences obtained in the decomposition step. + + Each butterfly operation involves multiplication by a "twiddle factor," which is a root of unity in the finite field, and addition or subtraction of the results, all performed modulo the prime modulus. + + $$ + X_k = (A_k + B_k \cdot W^k) \mod p + $$ + + $X_k$ - The output of the butterfly operation for the $k$-th element + + $A_k$ - an element from the even-indexed subset + + $B_k$ - an element from the odd-indexed subset + + $p$ - prime modulus + + $k$ - The index of the current operation within the butterfly or the transform stage + + + The twiddle factors are precomputed to save runtime and improve performance. + +4. **Bit-Reversal Permutation:** + A final step involves rearranging the output sequence into the correct order. Due to the halving process in the decomposition steps, the elements of the transformed sequence are initially in a bit-reversed order. A bit-reversal permutation is applied to obtain the final sequence in natural order. + +### Mixed Radix + +The Mixed Radix NTT algorithm extends the concepts of the Radix-2 algorithm by allowing the decomposition of the input sequence based on various factors of its length. Specifically ICICLEs implementation splits the input into blocks of sizes 16,32,64 compared to radix2 which is always splitting such that we end with NTT of size 2. This approach offers enhanced flexibility and efficiency, especially for input sizes that are composite numbers, by leveraging the "divide and conquer" strategy across multiple radixes. + +The NTT blocks in Mixed Radix are implemented more efficiently based on winograd NTT but also optimized memory and register usage is better compared to Radix-2. + +Mixed Radix can reduce the number of stages required to compute for large inputs. + +1. **Input Preparation:** + The input to the Mixed Radix NTT is a sequence of integers $a_0, a_1, \ldots, a_{N-1}$, where $N$ is not strictly required to be a power of two. Instead, $N$ can be any composite number, ideally factorized into primes or powers of primes. + +2. **Factorization and Decomposition:** + Unlike the Radix-2 algorithm, which strictly divides the computational problem into halves, the Mixed Radix NTT algorithm implements a flexible decomposition approach which isn't limited to prime factorization. + + For example, an NTT of size 256 can be decomposed into two stages of $16 \times \text{NTT}_{16}$, leveraging a composite factorization strategy rather than decomposing into eight stages of $\text{NTT}_{2}$. This exemplifies the use of composite factors (in this case, $256 = 16 \times 16$) to apply smaller NTT transforms, optimizing computational efficiency by adapting the decomposition strategy to the specific structure of $N$. + +3. **Butterfly Operations with Multiple Radixes:** + The Mixed Radix algorithm utilizes butterfly operations for various radix sizes. Each sub-transform involves specific butterfly operations characterized by multiplication with twiddle factors appropriate for the radix in question. + + The generalized butterfly operation for a radix-$r$ element can be expressed as: + + $$ + X_{k,r} = \sum_{j=0}^{r-1} (A_{j,k} \cdot W^{jk}) \mod p + $$ + + where $X_{k,r}$ is the output of the $radix-r$ butterfly operation for the $k-th$ set of inputs, $A_{j,k}$ represents the $j-th$ input element for the $k-th$ operation, $W$ is the twiddle factor, and $p$ is the prime modulus. + +4. **Recombination and Reordering:** + After applying the appropriate butterfly operations across all decomposition levels, the Mixed Radix algorithm recombines the results into a single output sequence. Due to the varied sizes of the sub-transforms, a more complex reordering process may be required compared to Radix-2. This involves digit-reversal permutations to ensure that the final output sequence is correctly ordered. + +### Which algorithm should I choose ? + +Both work only on inputs of power of 2 (e.g., 256, 512, 1024). + +Radix 2 is faster for small NTTs. A small NTT would be around logN = 16 and batch size 1. Radix 2 won't necessarily perform better for smaller `logn` with larger batches. + +Mixed radix on the other hand works better for larger NTTs with larger input sizes. + +Performance really depends on logn size, batch size, ordering, inverse, coset, coeff-field and which GPU you are using. + +For this reason we implemented our [heuristic auto-selection](https://github.com/ingonyama-zk/icicle/blob/774250926c00ffe84548bc7dd97aea5227afed7e/icicle/appUtils/ntt/ntt.cu#L474) which should choose the most efficient algorithm in most cases. + +We still recommend you benchmark for your specific use case if you think a different configuration would yield better results. diff --git a/docs/versioned_docs/version-1.10.1/icicle/primitives/overview.md b/docs/versioned_docs/version-1.10.1/icicle/primitives/overview.md new file mode 100644 index 000000000..c21add02b --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/primitives/overview.md @@ -0,0 +1,11 @@ +# ICICLE Primitives + +This section of the documentation is dedicated to the ICICLE primitives, we will cover the usage and internal details of our primitives such as hashing algorithms, MSM and NTT. + + +## Supported primitives + + +- [MSM](./msm.md) +- [NTT](./ntt.md) +- [Poseidon Hash](./poseidon.md) diff --git a/docs/versioned_docs/version-1.10.1/icicle/primitives/poseidon.md b/docs/versioned_docs/version-1.10.1/icicle/primitives/poseidon.md new file mode 100644 index 000000000..44cca473d --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/primitives/poseidon.md @@ -0,0 +1,226 @@ +# Poseidon + +[Poseidon](https://eprint.iacr.org/2019/458.pdf) is a popular hash in the ZK ecosystem primarily because its optimized to work over large prime fields, a common setting for ZK proofs, thereby minimizing the number of multiplicative operations required. + +Poseidon has also been specifically designed to be efficient when implemented within ZK circuits, Poseidon uses far less constraints compared to other hash functions like Keccak or SHA-256 in the context of ZK circuits. + +Poseidon has been used in many popular ZK protocols such as Filecoin and [Plonk](https://drive.google.com/file/d/1bZZvKMQHaZGA4L9eZhupQLyGINkkFG_b/view?usp=drive_open). + +Our implementation of Poseidon is implemented in accordance with the optimized [Filecoin version](https://spec.filecoin.io/algorithms/crypto/poseidon/). + +Let understand how Poseidon works. + +### Initialization + +Poseidon starts with the initialization of its internal state, which is composed of the input elements and some pregenerated constants. An initial round constant is added to each element of the internal state. Adding The round constants ensure the state is properly mixed from the outset. + +This is done to prevent collisions and to prevent certain cryptographic attacks by ensuring that the internal state is sufficiently mixed and unpredictable. + +![Alt text](image.png) + +### Applying full and partial rounds + +To generate a secure hash output, the algorithm goes through a series of "full rounds" and "partial rounds" as well as transformations between these sets of rounds. + +First full rounds => apply SBox and Round constants => partial rounds => Last full rounds => Apply SBox + +#### Full rounds + +![Alt text](image-1.png) + +**Uniform Application of S-Box:** In full rounds, the S-box (a non-linear transformation) is applied uniformly to every element of the hash function's internal state. This ensures a high degree of mixing and diffusion, contributing to the hash function's security. The functions S-box involves raising each element of the state to a certain power denoted by `α` a member of the finite field defined by the prime `p`, `α` can be different depending on the the implementation and user configuration. + +**Linear Transformation:** After applying the S-box, a linear transformation is performed on the state. This involves multiplying the state by a MDS (Maximum Distance Separable) Matrix. which further diffuses the transformations applied by the S-box across the entire state. + +**Addition of Round Constants:** Each element of the state is then modified by adding a unique round constant. These constants are different for each round and are precomputed as part of the hash function's initialization. The addition of round constants ensures that even minor changes to the input produce significant differences in the output. + +#### Partial Rounds + +**Selective Application of S-Box:** Partial rounds apply the S-box transformation to only one element of the internal state per round, rather than to all elements. This selective application significantly reduces the computational complexity of the hash function without compromising its security. The choice of which element to apply the S-box to can follow a specific pattern or be fixed, depending on the design of the hash function. + +**Linear Transformation and Round Constants:** A linear transformation is performed and round constants are added. The linear transformation in partial rounds can be designed to be less computationally intensive (this is done by using a sparse matrix) than in full rounds, further optimizing the function's efficiency. + + +The user of Poseidon can often choose how many partial or full rounds he wishes to apply; more full rounds will increase security but degrade performance. The choice and balance is highly dependent on the use case. + +![Alt text](image-2.png) + +## Using Poseidon + +ICICLE Poseidon is implemented for GPU and parallelization is performed for each element of the state rather than for each state. +What that means is we calculate multiple hash-sums over multiple pre-images in parallel, rather than going block by block over the input vector. + +So for Poseidon of arity 2 and input of size 1024 * 2, we would expect 1024 elements of output. Which means each block would be of size 2 and that would result in 1024 Poseidon hashes being performed. + +### Supported API + +[`Rust`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/icicle-core/src/poseidon), [`C++`](https://github.com/ingonyama-zk/icicle/tree/main/icicle/appUtils/poseidon) + +### Supported curves + +Poseidon supports the following curves: + +`bls12-377`, `bls12-381`, `bn-254`, `bw6-761` + +### Constants + +Poseidon is extremely customizable and using different constants will produce different hashes, security levels and performance results. + +We support pre-calculated and optimized constants for each of the [supported curves](#supported-curves).The constants can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/appUtils/poseidon/constants) and are labeled clearly per curve `_poseidon.h`. + +If you wish to generate your own constants you can use our python script which can be found [here](https://github.com/ingonyama-zk/icicle/blob/b6dded89cdef18348a5d4e2748b71ce4211c63ad/icicle/appUtils/poseidon/constants/generate_parameters.py#L1). + +Prerequisites: +- Install python 3 +- `pip install poseidon-hash` +- `pip install galois==0.3.7` +- `pip install numpy` + +You will then need to modify the following values before running the script. + +```python +# Modify these +arity = 11 # we support arity 2, 4, 8 and 11. +p = 0x73EDA753299D7D483339D80809A1D80553BDA402FFFE5BFEFFFFFFFF00000001 # bls12-381 +# p = 0x12ab655e9a2ca55660b44d1e5c37b00159aa76fed00000010a11800000000001 # bls12-377 +# p = 0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001 # bn254 +# p = 0x1ae3a4617c510eac63b05c06ca1493b1a22d9f300f5138f1ef3622fba094800170b5d44300000008508c00000000001 # bw6-761 +prime_bit_len = 255 +field_bytes = 32 + +... + +# primitive_element = None +primitive_element = 7 # bls12-381 +# primitive_element = 22 # bls12-377 +# primitive_element = 5 # bn254 +# primitive_element = 15 # bw6-761 +``` + +We only support `alpha = 5` so if you want to use another alpha for SBox please reach out on discord or open a github issue. + +### Rust API + +This is the most basic way to use the Poseidon API. + +```rust +let test_size = 1 << 10; +let arity = 2u32; +let ctx = get_default_device_context(); +let constants = load_optimized_poseidon_constants::(arity, &ctx).unwrap(); +let config = PoseidonConfig::default(); + +let inputs = vec![F::one(); test_size * arity as usize]; +let outputs = vec![F::zero(); test_size]; +let mut input_slice = HostOrDeviceSlice::on_host(inputs); +let mut output_slice = HostOrDeviceSlice::on_host(outputs); + +poseidon_hash_many::( + &mut input_slice, + &mut output_slice, + test_size as u32, + arity as u32, + &constants, + &config, +) +.unwrap(); +``` + +The `PoseidonConfig::default()` can be modified, by default the inputs and outputs are set to be on `Host` for example. + + +``` +impl<'a> Default for PoseidonConfig<'a> { + fn default() -> Self { + let ctx = get_default_device_context(); + Self { + ctx, + are_inputs_on_device: false, + are_outputs_on_device: false, + input_is_a_state: false, + aligned: false, + loop_state: false, + is_async: false, + } + } +} +``` + +In the example above `load_optimized_poseidon_constants::(arity, &ctx).unwrap();` is used which will load the correct constants based on arity and curve. Its possible to [generate](#constants) your own constants and load them. + +```rust +let ctx = get_default_device_context(); + let cargo_manifest_dir = env!("CARGO_MANIFEST_DIR"); + let constants_file = PathBuf::from(cargo_manifest_dir) + .join("tests") + .join(format!("{}_constants.bin", field_prefix)); + let mut constants_buf = vec![]; + File::open(constants_file) + .unwrap() + .read_to_end(&mut constants_buf) + .unwrap(); + + let mut custom_constants = vec![]; + for chunk in constants_buf.chunks(field_bytes) { + custom_constants.push(F::from_bytes_le(chunk)); + } + + let custom_constants = create_optimized_poseidon_constants::( + arity as u32, + &ctx, + full_rounds_half, + partial_rounds, + &mut custom_constants, + ) + .unwrap(); +``` +For more examples using different configurations refer here. + +## The Tree Builder + +The tree builder allows you to build Merkle trees using Poseidon. + +You can define both the tree's `height` and its `arity`. The tree `height` determines the number of layers in the tree, including the root and the leaf layer. The `arity` determines how many children each internal node can have. + +```rust +let height = 20; +let arity = 2; +let leaves = vec![F::one(); 1 << (height - 1)]; +let mut digests = vec![F::zero(); merkle_tree_digests_len(height, arity)]; + +let mut leaves_slice = HostOrDeviceSlice::on_host(leaves); + +let ctx = get_default_device_context(); +let constants = load_optimized_poseidon_constants::(arity, &ctx).unwrap() + +let mut config = TreeBuilderConfig::default(); +config.keep_rows = 1; +build_poseidon_merkle_tree::(&mut leaves_slice, &mut digests, height, arity, &constants, &config).unwrap(); + +println!("Root: {:?}", digests[0..1][0]); +``` + +Similar to Poseidon, you can also configure the Tree Builder `TreeBuilderConfig::default()` + +- `keep_rows`: The number of rows which will be written to output, 0 will write all rows. +- `are_inputs_on_device`: Have the inputs been loaded to device memory ? +- `is_async`: Should the TreeBuilder run asynchronously? `False` will block the current CPU thread. `True` will require you call `cudaStreamSynchronize` or `cudaDeviceSynchronize` to retrieve the result. + +### Benchmarks + +We ran the Poseidon tree builder on: + +**CPU**: 12th Gen Intel(R) Core(TM) i9-12900K/ + +**GPU**: RTX 3090 Ti + +**Tree height**: 30 (2^29 elements) + +The benchmarks include copying data from and to the device. + + +| Rows to keep parameter | Run time, Icicle | Supranational PC2 +| ----------- | ----------- | ----------- | +| 10 | 9.4 seconds | 13.6 seconds +| 20 | 9.5 seconds | 13.6 seconds +| 29 | 13.7 seconds | 13.6 seconds diff --git a/docs/versioned_docs/version-1.10.1/icicle/rust-bindings.md b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings.md new file mode 100644 index 000000000..6515550dd --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings.md @@ -0,0 +1,57 @@ +# Rust bindings + +Rust bindings allow you to use ICICLE as a rust library. + +`icicle-core` defines all interfaces, macros and common methods. + +`icicle-cuda-runtime` defines DeviceContext which can be used to manage a specific GPU as well as wrapping common CUDA methods. + +`icicle-curves` implements all interfaces and macros from icicle-core for each curve. For example icicle-bn254 implements curve bn254. Each curve has its own build script which will build the CUDA libraries for that curve as part of the rust-toolchain build. + +## Using ICICLE Rust bindings in your project + +Simply add the following to your `Cargo.toml`. + +``` +# GPU Icicle integration +icicle-cuda-runtime = { git = "https://github.com/ingonyama-zk/icicle.git" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git" } +icicle-bn254 = { git = "https://github.com/ingonyama-zk/icicle.git" } +``` + +`icicle-bn254` being the curve you wish to use and `icicle-core` and `icicle-cuda-runtime` contain ICICLE utilities and CUDA wrappers. + +If you wish to point to a specific ICICLE branch add `branch = ""` or `tag = ""` to the ICICLE dependency. For a specific commit add `rev = ""`. + +When you build your project ICICLE will be built as part of the build command. + +# How do the rust bindings work? + +The rust bindings are just rust wrappers for ICICLE Core static libraries which can be compiled. We integrate the compilation of the static libraries into rusts toolchain to make usage seamless and easy. This is achieved by [extending rusts build command](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-curves/icicle-bn254/build.rs). + +```rust +use cmake::Config; +use std::env::var; + +fn main() { + println!("cargo:rerun-if-env-changed=CXXFLAGS"); + println!("cargo:rerun-if-changed=../../../../icicle"); + + let cargo_dir = var("CARGO_MANIFEST_DIR").unwrap(); + let profile = var("PROFILE").unwrap(); + + let out_dir = Config::new("../../../../icicle") + .define("BUILD_TESTS", "OFF") //TODO: feature + .define("CURVE", "bn254") + .define("CMAKE_BUILD_TYPE", "Release") + .build_target("icicle") + .build(); + + println!("cargo:rustc-link-search={}/build", out_dir.display()); + + println!("cargo:rustc-link-lib=ingo_bn254"); + println!("cargo:rustc-link-lib=stdc++"); + // println!("cargo:rustc-link-search=native=/usr/local/cuda/lib64"); + println!("cargo:rustc-link-lib=cudart"); +} +``` diff --git a/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/msm-pre-computation.md b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/msm-pre-computation.md new file mode 100644 index 000000000..c0cc941b6 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/msm-pre-computation.md @@ -0,0 +1,63 @@ +# MSM Pre computation + +To understand the theory behind MSM pre computation technique refer to Niall Emmart's [talk](https://youtu.be/KAWlySN7Hm8?feature=shared&t=1734). + +### Supported curves + +`bls12-377`, `bls12-381`, `bn254`, `bw6-761`, `Grumpkin` + +### `precompute_bases` + +Precomputes bases for the multi-scalar multiplication (MSM) by extending each base point with its multiples, facilitating more efficient MSM calculations. + +```rust +pub fn precompute_bases>( + points: &HostOrDeviceSlice>, + precompute_factor: i32, + _c: i32, + ctx: &DeviceContext, + output_bases: &mut HostOrDeviceSlice>, +) -> IcicleResult<()> +``` + + +#### Parameters + +- **`points`**: The original set of affine points (\(P_1, P_2, ..., P_n\)) to be used in the MSM. For batch MSM operations, this should include all unique points concatenated together. +- **`precompute_factor`**: Specifies the total number of points to precompute for each base, including the base point itself. This parameter directly influences the memory requirements and the potential speedup of the MSM operation. +- **`_c`**: Currently unused. Intended for future use to align with the `c` parameter in `MSMConfig`, ensuring the precomputation is compatible with the bucket method's window size used in MSM. +- **`ctx`**: The device context specifying the device ID and stream for execution. This context determines where the precomputation is performed (e.g., on a specific GPU). +- **`output_bases`**: The output buffer for the extended bases. Its size must be `points.len() * precompute_factor`. This buffer should be allocated on the device for GPU computations. + +#### Returns + +`Ok(())` if the operation is successful, or an `IcicleResult` error otherwise. + +#### Description + +This function extends each provided base point $(P)$ with its multiples $(2^lP, 2^{2l}P, ..., 2^{(precompute_factor - 1) \cdot l}P)$, where $(l)$ is a level of precomputation determined by the `precompute_factor`. The extended set of points facilitates faster MSM computations by allowing the MSM algorithm to leverage precomputed multiples of base points, reducing the number of point additions required during the computation. + +The precomputation process is crucial for optimizing MSM operations, especially when dealing with large sets of points and scalars. By precomputing and storing multiples of the base points, the MSM function can more efficiently compute the scalar-point multiplications. + +#### Example Usage + +```rust +let device_context = DeviceContext::default_for_device(0); // Use the default device +let precompute_factor = 4; // Number of points to precompute +let mut extended_bases = HostOrDeviceSlice::cuda_malloc(expected_size).expect("Failed to allocate memory for extended bases"); + +// Precompute the bases using the specified factor +precompute_bases(&points, precompute_factor, 0, &device_context, &mut extended_bases) + .expect("Failed to precompute bases"); +``` + +### Benchmarks + +Benchmarks where performed on a Nvidia RTX 3090Ti. + +| Pre-computation factor | bn254 size `2^20` MSM, ms. | bn254 size `2^12` MSM, size `2^10` batch, ms. | bls12-381 size `2^20` MSM, ms. | bls12-381 size `2^12` MSM, size `2^10` batch, ms. | +| ------------- | ------------- | ------------- | ------------- | ------------- | +| 1 | 14.1 | 82.8 | 25.5 | 136.7 | +| 2 | 11.8 | 76.6 | 20.3 | 123.8 | +| 4 | 10.9 | 73.8 | 18.1 | 117.8 | +| 8 | 10.6 | 73.7 | 17.2 | 116.0 | diff --git a/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/msm.md b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/msm.md new file mode 100644 index 000000000..4425ba3c0 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/msm.md @@ -0,0 +1,172 @@ +# MSM + +### Supported curves + +`bls12-377`, `bls12-381`, `bn-254`, `bw6-761`, `grumpkin` + +## Example + +```rust +use icicle_bn254::curve::{CurveCfg, G1Projective, ScalarCfg}; +use icicle_core::{curve::Curve, msm, traits::GenerateRandom}; +use icicle_cuda_runtime::{memory::HostOrDeviceSlice, stream::CudaStream}; + +fn main() { + let size: usize = 1 << 10; // Define the number of points and scalars + + // Generate random points and scalars + println!("Generating random G1 points and scalars for BN254..."); + let points = CurveCfg::generate_random_affine_points(size); + let scalars = ScalarCfg::generate_random(size); + + // Wrap points and scalars in HostOrDeviceSlice for MSM + let points_host = HostOrDeviceSlice::Host(points); + let scalars_host = HostOrDeviceSlice::Host(scalars); + + // Allocate memory on the CUDA device for MSM results + let mut msm_results: HostOrDeviceSlice<'_, G1Projective> = HostOrDeviceSlice::cuda_malloc(1).expect("Failed to allocate CUDA memory for MSM results"); + + // Create a CUDA stream for asynchronous execution + let stream = CudaStream::create().expect("Failed to create CUDA stream"); + let mut cfg = msm::MSMConfig::default(); + cfg.ctx.stream = &stream; + cfg.is_async = true; // Enable asynchronous execution + + // Execute MSM on the device + println!("Executing MSM on device..."); + msm::msm(&scalars_host, &points_host, &cfg, &mut msm_results).expect("Failed to execute MSM"); + + // Synchronize CUDA stream to ensure MSM execution is complete + stream.synchronize().expect("Failed to synchronize CUDA stream"); + + // Optionally, move results to host for further processing or printing + println!("MSM execution complete."); +} +``` + +## MSM API Overview + +```rust +pub fn msm( + scalars: &HostOrDeviceSlice, + points: &HostOrDeviceSlice>, + cfg: &MSMConfig, + results: &mut HostOrDeviceSlice>, +) -> IcicleResult<()> +``` + +### Parameters + +- **`scalars`**: A buffer containing the scalar values to be multiplied with corresponding points. +- **`points`**: A buffer containing the points to be multiplied by the scalars. +- **`cfg`**: MSM configuration specifying additional parameters for the operation. +- **`results`**: A buffer where the results of the MSM operations will be stored. + +### MSM Config + +```rust +pub struct MSMConfig<'a> { + pub ctx: DeviceContext<'a>, + points_size: i32, + pub precompute_factor: i32, + pub c: i32, + pub bitsize: i32, + pub large_bucket_factor: i32, + batch_size: i32, + are_scalars_on_device: bool, + pub are_scalars_montgomery_form: bool, + are_points_on_device: bool, + pub are_points_montgomery_form: bool, + are_results_on_device: bool, + pub is_big_triangle: bool, + pub is_async: bool, +} +``` + +- **`ctx: DeviceContext`**: Specifies the device context, device id and the CUDA stream for asynchronous execution. +- **`point_size: i32`**: +- **`precompute_factor: i32`**: Determines the number of extra points to pre-compute for each point, affecting memory footprint and performance. +- **`c: i32`**: The "window bitsize," a parameter controlling the computational complexity and memory footprint of the MSM operation. +- **`bitsize: i32`**: The number of bits of the largest scalar, typically equal to the bit size of the scalar field. +- **`large_bucket_factor: i32`**: Adjusts the algorithm's sensitivity to frequently occurring buckets, useful for non-uniform scalar distributions. +- **`batch_size: i32`**: The number of MSMs to compute in a single batch, for leveraging parallelism. +- **`are_scalars_montgomery_form`**: Set to `true` if scalars are in montgomery form. +- **`are_points_montgomery_form`**: Set to `true` if points are in montgomery form. +- **`are_scalars_on_device: bool`**, **`are_points_on_device: bool`**, **`are_results_on_device: bool`**: Indicate whether the corresponding buffers are on the device memory. +- **`is_big_triangle`**: If `true` MSM will run in Large triangle accumulation if `false` Bucket accumulation will be chosen. Default value: false. +- **`is_async: bool`**: Whether to perform the MSM operation asynchronously. + +### Usage + +The `msm` function is designed to compute the sum of multiple scalar-point multiplications efficiently. It supports both single MSM operations and batched operations for increased performance. The configuration allows for detailed control over the execution environment and performance characteristics of the MSM operation. + +When performing MSM operations, it's crucial to match the size of the `scalars` and `points` arrays correctly and ensure that the `results` buffer is appropriately sized to hold the output. The `MSMConfig` should be set up to reflect the specifics of the operation, including whether the operation should be asynchronous and any device-specific settings. + +## How do I toggle between the supported algorithms? + +When creating your MSM Config you may state which algorithm you wish to use. `is_big_triangle=true` will activate Large triangle accumulation and `is_big_triangle=false` will activate Bucket accumulation. + +```rust +... + +let mut cfg_bls12377 = msm::get_default_msm_config::(); + +// is_big_triangle will determine which algorithm to use +cfg_bls12377.is_big_triangle = true; + +msm::msm(&scalars, &points, &cfg, &mut msm_results).unwrap(); +... +``` + +You may reference the rust code [here](https://github.com/ingonyama-zk/icicle/blob/77a7613aa21961030e4e12bf1c9a78a2dadb2518/wrappers/rust/icicle-core/src/msm/mod.rs#L54). + + +## How do I toggle between MSM modes? + +Toggling between MSM modes occurs automatically based on the number of results you are expecting from the `msm::msm` function. If you are expecting an array of `msm_results`, ICICLE will automatically split `scalars` and `points` into equal parts and run them as multiple MSMs in parallel. + +```rust +... + +let mut msm_result: HostOrDeviceSlice<'_, G1Projective> = HostOrDeviceSlice::cuda_malloc(1).unwrap(); +msm::msm(&scalars, &points, &cfg, &mut msm_result).unwrap(); + +... +``` + +In the example above we allocate a single expected result which the MSM method will interpret as `batch_size=1` and run a single MSM. + + +In the next example, we are expecting 10 results which sets `batch_size=10` and runs 10 MSMs in batch mode. + +```rust +... + +let mut msm_results: HostOrDeviceSlice<'_, G1Projective> = HostOrDeviceSlice::cuda_malloc(10).unwrap(); +msm::msm(&scalars, &points, &cfg, &mut msm_results).unwrap(); + +... +``` + +Here is a [reference](https://github.com/ingonyama-zk/icicle/blob/77a7613aa21961030e4e12bf1c9a78a2dadb2518/wrappers/rust/icicle-core/src/msm/mod.rs#L108) to the code which automatically sets the batch size. For more MSM examples have a look [here](https://github.com/ingonyama-zk/icicle/blob/77a7613aa21961030e4e12bf1c9a78a2dadb2518/examples/rust/msm/src/main.rs#L1). + +## Support for G2 group + +MSM also supports G2 group. + +Using MSM in G2 requires a G2 config, and of course your Points should also be G2 Points. + +```rust +... + +let scalars = HostOrDeviceSlice::Host(upper_scalars[..size].to_vec()); +let g2_points = HostOrDeviceSlice::Host(g2_upper_points[..size].to_vec()); +let mut g2_msm_results: HostOrDeviceSlice<'_, G2Projective> = HostOrDeviceSlice::cuda_malloc(1).unwrap(); +let mut g2_cfg = msm::get_default_msm_config::(); + +msm::msm(&scalars, &g2_points, &g2_cfg, &mut g2_msm_results).unwrap(); + +... +``` + +Here you can [find an example](https://github.com/ingonyama-zk/icicle/blob/5a96f9937d0a7176d88c766bd3ef2062b0c26c37/examples/rust/msm/src/main.rs#L114) of MSM on G2 Points. diff --git a/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/multi-gpu.md b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/multi-gpu.md new file mode 100644 index 000000000..685d5cd4d --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/multi-gpu.md @@ -0,0 +1,202 @@ +# Multi GPU APIs + +To learn more about the theory of Multi GPU programming refer to [this part](../multi-gpu.md) of documentation. + +Here we will cover the core multi GPU apis and a [example](#a-multi-gpu-example) + + +## A Multi GPU example + +In this example we will display how you can + +1. Fetch the number of devices installed on a machine +2. For every GPU launch a thread and set an active device per thread. +3. Execute a MSM on each GPU + + + +```rust + +... + +let device_count = get_device_count().unwrap(); + +(0..device_count) + .into_par_iter() + .for_each(move |device_id| { + set_device(device_id).unwrap(); + + // you can allocate points and scalars_d here + + let mut cfg = MSMConfig::default_for_device(device_id); + cfg.ctx.stream = &stream; + cfg.is_async = true; + cfg.are_scalars_montgomery_form = true; + msm(&scalars_d, &HostOrDeviceSlice::on_host(points), &cfg, &mut msm_results).unwrap(); + + // collect and process results + }) + +... +``` + + +We use `get_device_count` to fetch the number of connected devices, device IDs will be `0, 1, 2, ..., device_count - 1` + +[`into_par_iter`](https://docs.rs/rayon/latest/rayon/iter/trait.IntoParallelIterator.html#tymethod.into_par_iter) is a parallel iterator, you should expect it to launch a thread for every iteration. + +We then call `set_device(device_id).unwrap();` it should set the context of that thread to the selected `device_id`. + +Any data you now allocate from the context of this thread will be linked to the `device_id`. We create our `MSMConfig` with the selected device ID `let mut cfg = MSMConfig::default_for_device(device_id);`, behind the scene this will create for us a `DeviceContext` configured for that specific GPU. + +We finally call our `msm` method. + + +## Device management API + +To streamline device management we offer as part of `icicle-cuda-runtime` package methods for dealing with devices. + +#### [`set_device`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L6) + +Sets the current CUDA device by its ID, when calling `set_device` it will set the current thread to a CUDA device. + +**Parameters:** + +- `device_id: usize`: The ID of the device to set as the current device. Device IDs start from 0. + +**Returns:** + +- `CudaResult<()>`: An empty result indicating success if the device is set successfully. In case of failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if the specified device ID is invalid or if a CUDA-related error occurs during the operation. + +**Example:** + +```rust +let device_id = 0; // Device ID to set +match set_device(device_id) { + Ok(()) => println!("Device set successfully."), + Err(e) => eprintln!("Failed to set device: {:?}", e), +} +``` + +#### [`get_device_count`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L10) + +Retrieves the number of CUDA devices available on the machine. + +**Returns:** + +- `CudaResult`: The number of available CUDA devices. On success, contains the count of CUDA devices. On failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if a CUDA-related error occurs during the retrieval of the device count. + +**Example:** + +```rust +match get_device_count() { + Ok(count) => println!("Number of devices available: {}", count), + Err(e) => eprintln!("Failed to get device count: {:?}", e), +} +``` + +#### [`get_device`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L15) + +Retrieves the ID of the current CUDA device. + +**Returns:** + +- `CudaResult`: The ID of the current CUDA device. On success, contains the device ID. On failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if a CUDA-related error occurs during the retrieval of the current device ID. + +**Example:** + +```rust +match get_device() { + Ok(device_id) => println!("Current device ID: {}", device_id), + Err(e) => eprintln!("Failed to get current device: {:?}", e), +} +``` + +## Device context API + +The `DeviceContext` is embedded into `NTTConfig`, `MSMConfig` and `PoseidonConfig`, meaning you can simply pass a `device_id` to your existing config and the same computation will be triggered on a different device. + +#### [`DeviceContext`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L11) + +Represents the configuration a CUDA device, encapsulating the device's stream, ID, and memory pool. The default device is always `0`. + +```rust +pub struct DeviceContext<'a> { + pub stream: &'a CudaStream, + pub device_id: usize, + pub mempool: CudaMemPool, +} +``` + +##### Fields + +- **`stream: &'a CudaStream`** + + A reference to a `CudaStream`. This stream is used for executing CUDA operations. By default, it points to a null stream CUDA's default execution stream. + +- **`device_id: usize`** + + The index of the GPU currently in use. The default value is `0`, indicating the first GPU in the system. + + In some cases assuming `CUDA_VISIBLE_DEVICES` was configured, for example as `CUDA_VISIBLE_DEVICES=2,3,7` in the system with 8 GPUs - the `device_id=0` will correspond to GPU with id 2. So the mapping may not always be a direct reflection of the number of GPUs installed on a system. + +- **`mempool: CudaMemPool`** + + Represents the memory pool used for CUDA memory allocations. The default is set to a null pointer, which signifies the use of the default CUDA memory pool. + +##### Implementation Notes + +- The `DeviceContext` structure is cloneable and can be debugged, facilitating easier logging and duplication of contexts when needed. + + +#### [`DeviceContext::default_for_device(device_id: usize) -> DeviceContext<'static>`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L30) + +Provides a default `DeviceContext` with system-wide defaults, ideal for straightforward setups. + +#### Returns + +A `DeviceContext` instance configured with: +- The default stream (`null_mut()`). +- The default device ID (`0`). +- The default memory pool (`null_mut()`). + +#### Parameters + +- **`device_id: usize`**: The ID of the device for which to create the context. + +#### Returns + +A `DeviceContext` instance with the provided `device_id` and default settings for the stream and memory pool. + + +#### [`check_device(device_id: i32)`](https://github.com/vhnatyk/icicle/blob/eef6876b037a6b0797464e7cdcf9c1ecfcf41808/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L42) + +Validates that the specified `device_id` matches the ID of the currently active device, ensuring operations are targeted correctly. + +#### Parameters + +- **`device_id: i32`**: The device ID to verify against the currently active device. + +#### Behavior + +- **Panics** if the `device_id` does not match the active device's ID, preventing cross-device operation errors. + +#### Example + +```rust +let device_id: i32 = 0; // Example device ID +check_device(device_id); +// Ensures that the current context is correctly set for the specified device ID. +``` diff --git a/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/ntt.md b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/ntt.md new file mode 100644 index 000000000..8bde41292 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/ntt.md @@ -0,0 +1,199 @@ +# NTT + +### Supported curves + +`bls12-377`, `bls12-381`, `bn-254`, `bw6-761` + +## Example + +```rust +use icicle_bn254::curve::{ScalarCfg, ScalarField}; +use icicle_core::{ntt::{self, NTT}, traits::GenerateRandom}; +use icicle_cuda_runtime::{device_context::DeviceContext, memory::HostOrDeviceSlice, stream::CudaStream}; + +fn main() { + let size = 1 << 12; // Define the size of your input, e.g., 2^10 + + let icicle_omega = ::get_root_of_unity( + size.try_into() + .unwrap(), + ) + + // Generate random inputs + println!("Generating random inputs..."); + let scalars = HostOrDeviceSlice::Host(ScalarCfg::generate_random(size)); + + // Allocate memory on CUDA device for NTT results + let mut ntt_results: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::cuda_malloc(size).expect("Failed to allocate CUDA memory"); + + // Create a CUDA stream + let stream = CudaStream::create().expect("Failed to create CUDA stream"); + let ctx = DeviceContext::default(); // Assuming default device context + ScalarCfg::initialize_domain(ScalarField::from_ark(icicle_omega), &ctx).unwrap(); + + // Configure NTT + let mut cfg = ntt::NTTConfig::default(); + cfg.ctx.stream = &stream; + cfg.is_async = true; // Set to true for asynchronous execution + + // Execute NTT on device + println!("Executing NTT on device..."); + ntt::ntt(&scalars, ntt::NTTDir::kForward, &cfg, &mut ntt_results).expect("Failed to execute NTT"); + + // Synchronize CUDA stream to ensure completion + stream.synchronize().expect("Failed to synchronize CUDA stream"); + + // Optionally, move results to host for further processing or verification + println!("NTT execution complete."); +} +``` + +## NTT API overview + +```rust +pub fn ntt( + input: &HostOrDeviceSlice, + dir: NTTDir, + cfg: &NTTConfig, + output: &mut HostOrDeviceSlice, +) -> IcicleResult<()> +``` + +`ntt:ntt` expects: + +`input` - buffer to read the inputs of the NTT from.
+`dir` - whether to compute forward or inverse NTT.
+`cfg` - config used to specify extra arguments of the NTT.
+`output` - buffer to write the NTT outputs into. Must be of the same size as input. + +The `input` and `output` buffers can be on device or on host. Being on host means that they will be transferred to device during runtime. + + +### NTT Config + +```rust +pub struct NTTConfig<'a, S> { + pub ctx: DeviceContext<'a>, + pub coset_gen: S, + pub batch_size: i32, + pub columns_batch: bool, + pub ordering: Ordering, + are_inputs_on_device: bool, + are_outputs_on_device: bool, + pub is_async: bool, + pub ntt_algorithm: NttAlgorithm, +} +``` + +The `NTTConfig` struct is a configuration object used to specify parameters for an NTT instance. + +#### Fields + +- **`ctx: DeviceContext<'a>`**: Specifies the device context, including the device ID and the stream ID. + +- **`coset_gen: S`**: Defines the coset generator used for coset (i)NTTs. By default, this is set to `S::one()`, indicating that no coset is being used. + +- **`batch_size: i32`**: Determines the number of NTTs to compute in a single batch. The default value is 1, meaning that operations are performed on individual inputs without batching. Batch processing can significantly improve performance by leveraging parallelism in GPU computations. + +- **`columns_batch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. Defaults to `false`. + +- **`ordering: Ordering`**: Controls the ordering of inputs and outputs for the NTT operation. This field can be used to specify decimation strategies (in time or in frequency) and the type of butterfly algorithm (Cooley-Tukey or Gentleman-Sande). The ordering is crucial for compatibility with various algorithmic approaches and can impact the efficiency of the NTT. + +- **`are_inputs_on_device: bool`**: Indicates whether the input data has been preloaded on the device memory. If `false` inputs will be copied from host to device. + +- **`are_outputs_on_device: bool`**: Indicates whether the output data is preloaded in device memory. If `false` outputs will be copied from host to device. If the inputs and outputs are the same pointer NTT will be computed in place. + +- **`is_async: bool`**: Specifies whether the NTT operation should be performed asynchronously. When set to `true`, the NTT function will not block the CPU, allowing other operations to proceed concurrently. Asynchronous execution requires careful synchronization to ensure data integrity and correctness. + +- **`ntt_algorithm: NttAlgorithm`**: Can be one of `Auto`, `Radix2`, `MixedRadix`. +`Auto` will select `Radix 2` or `Mixed Radix` algorithm based on heuristics. +`Radix2` and `MixedRadix` will force the use of an algorithm regardless of the input size or other considerations. You should use one of these options when you know for sure that you want to + + +#### Usage + +Example initialization with default settings: + +```rust +let default_config = NTTConfig::default(); +``` + +Customizing the configuration: + +```rust +let custom_config = NTTConfig { + ctx: custom_device_context, + coset_gen: my_coset_generator, + batch_size: 10, + columns_batch: false, + ordering: Ordering::kRN, + are_inputs_on_device: true, + are_outputs_on_device: true, + is_async: false, + ntt_algorithm: NttAlgorithm::MixedRadix, +}; +``` + + +### Modes + +NTT supports two different modes `Batch NTT` and `Single NTT` + +You may toggle between single and batch NTT by simply configure `batch_size` to be larger then 1 in your `NTTConfig`. + +```rust +let mut cfg = ntt::get_default_ntt_config::(); +cfg.batch_size = 10 // your ntt using this config will run in batch mode. +``` + +`batch_size=1` would keep our NTT in single NTT mode. + +Deciding weather to use `batch NTT` vs `single NTT` is highly dependent on your application and use case. + +### Initializing the NTT Domain + +Before performing NTT operations, its necessary to initialize the NTT domain, It only needs to be called once per GPU since the twiddles are cached. + +```rust +ScalarCfg::initialize_domain(ScalarField::from_ark(icicle_omega), &ctx).unwrap(); +``` + +### `initialize_domain` + +```rust +pub fn initialize_domain(primitive_root: F, ctx: &DeviceContext) -> IcicleResult<()> +where + F: FieldImpl, + ::Config: NTT; +``` + +#### Parameters + +- **`primitive_root`**: The primitive root of unity, chosen based on the maximum NTT size required for the computations. It must be of an order that is a power of two. This root is used to generate twiddle factors that are essential for the NTT operations. + +- **`ctx`**: A reference to a `DeviceContext` specifying which device and stream the computation should be executed on. + +#### Returns + +- **`IcicleResult<()>`**: Will return an error if the operation fails. + +### `initialize_domain_fast_twiddles_mode` + +Similar to `initialize_domain`, `initialize_domain_fast_twiddles_mode` is a faster implementation and can be used for larger NTTs. + +```rust +pub fn initialize_domain_fast_twiddles_mode(primitive_root: F, ctx: &DeviceContext) -> IcicleResult<()> +where + F: FieldImpl, + ::Config: NTT; +``` + +#### Parameters + +- **`primitive_root`**: The primitive root of unity, chosen based on the maximum NTT size required for the computations. It must be of an order that is a power of two. This root is used to generate twiddle factors that are essential for the NTT operations. + +- **`ctx`**: A reference to a `DeviceContext` specifying which device and stream the computation should be executed on. + +#### Returns + +- **`IcicleResult<()>`**: Will return an error if the operation fails. diff --git a/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/vec-ops.md b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/vec-ops.md new file mode 100644 index 000000000..0f537edc9 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/rust-bindings/vec-ops.md @@ -0,0 +1,159 @@ +# Vector Operations API + +Our vector operations API which is part of `icicle-cuda-runtime` package, includes fundamental methods for addition, subtraction, and multiplication of vectors, with support for both host and device memory. + + +## Supported curves + +Vector operations are supported on the following curves: + +`bls12-377`, `bls12-381`, `bn-254`, `bw6-761`, `grumpkin` + +## Examples + +### Addition of Scalars + +```rust +use icicle_bn254::curve::{ScalarCfg, ScalarField}; +use icicle_core::vec_ops::{add_scalars}; + +let test_size = 1 << 18; + +let a: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); +let b: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); +let mut result: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::zero(); test_size]); + +let cfg = VecOpsConfig::default(); +add_scalars(&a, &b, &mut result, &cfg).unwrap(); +``` + +### Subtraction of Scalars + +```rust +use icicle_bn254::curve::{ScalarCfg, ScalarField}; +use icicle_core::vec_ops::{sub_scalars}; + +let test_size = 1 << 18; + +let a: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); +let b: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); +let mut result: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::zero(); test_size]); + +let cfg = VecOpsConfig::default(); +sub_scalars(&a, &b, &mut result, &cfg).unwrap(); +``` + +### Multiplication of Scalars + +```rust +use icicle_bn254::curve::{ScalarCfg, ScalarField}; +use icicle_core::vec_ops::{mul_scalars}; + +let test_size = 1 << 18; + +let a: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); +let ones: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::one(); test_size]); +let mut result: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::zero(); test_size]); + +let cfg = VecOpsConfig::default(); +mul_scalars(&a, &ones, &mut result, &cfg).unwrap(); +``` + + +## Vector Operations Configuration + +The `VecOpsConfig` struct encapsulates the settings for vector operations, including device context and operation modes. + +### `VecOpsConfig` + +Defines configuration parameters for vector operations. + +```rust +pub struct VecOpsConfig<'a> { + pub ctx: DeviceContext<'a>, + is_a_on_device: bool, + is_b_on_device: bool, + is_result_on_device: bool, + is_result_montgomery_form: bool, + pub is_async: bool, +} +``` + +#### Fields + +- **`ctx: DeviceContext<'a>`**: Specifies the device context for the operation, including the device ID and memory pool. +- **`is_a_on_device`**: Indicates if the first operand vector resides in device memory. +- **`is_b_on_device`**: Indicates if the second operand vector resides in device memory. +- **`is_result_on_device`**: Specifies if the result vector should be stored in device memory. +- **`is_result_montgomery_form`**: Determines if the result should be in Montgomery form. +- **`is_async`**: Enables asynchronous operation. If `true`, operations are non-blocking; otherwise, they block the current thread. + +### Default Configuration + +`VecOpsConfig` can be initialized with default settings tailored for a specific device: + +``` +let cfg = VecOpsConfig::default(); +``` + +These are the default settings. + +```rust +impl<'a> Default for VecOpsConfig<'a> { + fn default() -> Self { + Self::default_for_device(DEFAULT_DEVICE_ID) + } +} + +impl<'a> VecOpsConfig<'a> { + pub fn default_for_device(device_id: usize) -> Self { + VecOpsConfig { + ctx: DeviceContext::default_for_device(device_id), + is_a_on_device: false, + is_b_on_device: false, + is_result_on_device: false, + is_result_montgomery_form: false, + is_async: false, + } + } +} +``` + +## Vector Operations + +Vector operations are implemented through the `VecOps` trait, these traits are implemented for all [supported curves](#supported-curves) providing methods for addition, subtraction, and multiplication of vectors. + +### `VecOps` Trait + +```rust +pub trait VecOps { + fn add( + a: &HostOrDeviceSlice, + b: &HostOrDeviceSlice, + result: &mut HostOrDeviceSlice, + cfg: &VecOpsConfig, + ) -> IcicleResult<()>; + + fn sub( + a: &HostOrDeviceSlice, + b: &HostOrDeviceSlice, + result: &mut HostOrDeviceSlice, + cfg: &VecOpsConfig, + ) -> IcicleResult<()>; + + fn mul( + a: &HostOrDeviceSlice, + b: &HostOrDeviceSlice, + result: &mut HostOrDeviceSlice, + cfg: &VecOpsConfig, + ) -> IcicleResult<()>; +} +``` + +#### Methods + +All operations are element-wise operations, and the results placed into the `result` param. These operations are not in place. + +- **`add`**: Computes the element-wise sum of two vectors. +- **`sub`**: Computes the element-wise difference between two vectors. +- **`mul`**: Performs element-wise multiplication of two vectors. diff --git a/docs/versioned_docs/version-1.10.1/icicle/supporting-additional-curves.md b/docs/versioned_docs/version-1.10.1/icicle/supporting-additional-curves.md new file mode 100644 index 000000000..97b7b5567 --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/icicle/supporting-additional-curves.md @@ -0,0 +1,117 @@ +# Supporting Additional Curves + +We understand the need for ZK developers to use different curves, some common some more exotic. For this reason we designed ICICLE to allow developers to add any curve they desire. + +## ICICLE Core + +ICICLE core is very generic by design so all algorithms and primitives are designed to work based of configuration files [selected during compile](https://github.com/ingonyama-zk/icicle/blob/main/icicle/curves/curve_config.cuh) time. This is why we compile ICICLE Core per curve. + +To add support for a new curve you must create a new file under [`icicle/curves`](https://github.com/ingonyama-zk/icicle/tree/main/icicle/curves). The file should be named `_params.cuh`. + +### Adding curve_name_params.cuh + +Start by copying `bn254_params.cuh` contents in your params file. Params should include: + - **fq_config** - parameters of the Base field. + - **limbs_count** - `ceil(field_byte_size / 4)`. + - **modulus_bit_count** - bit-size of the modulus. + - **num_of_reductions** - the number of times to reduce in reduce function. Use 2 if not sure. + - **modulus** - modulus of the field. + - **modulus_2** - modulus * 2. + - **modulus_4** - modulus * 4. + - **neg_modulus** - negated modulus. + - **modulus_wide** - modulus represented as a double-sized integer. + - **modulus_squared** - modulus**2 represented as a double-sized integer. + - **modulus_squared_2** - 2 * modulus**2 represented as a double-sized integer. + - **modulus_squared_4** - 4 * modulus**2 represented as a double-sized integer. + - **m** - value used in multiplication. Can be computed as `2**(2*modulus_bit_count) // modulus`. + - **one** - multiplicative identity. + - **zero** - additive identity. + - **montgomery_r** - `2 ** M % modulus` where M is a closest (larger than) bitsize multiple of 32. E.g. 384 or 768 for bls and bw curves respectively + - **montgomery_r_inv** - `2 ** (-M) % modulus` + - **fp_config** - parameters of the Scalar field. + Same as fq_config, but with additional arguments: + - **omegas_count** - [two-adicity](https://cryptologie.net/article/559/whats-two-adicity/) of the field. And thus the maximum size of NTT. + - **omegas** - an array of omegas for NTTs. An array of size `omegas_count`. The ith element is equal to `1.nth_root(2**(2**(omegas_count-i)))`. + - **inv** - an array of inverses of powers of two in a field. Ith element is equal to `(2 ** (i+1)) ** -1`. + - **G1 generators points** - affine coordinates of the generator point. + - **G2 generators points** - affine coordinates of the extension generator. Remove these if `G2` is not supported. + - **Weierstrass b value** - base field element equal to value of `b` in the curve equation. + - **Weierstrass b value G2** - base field element equal to value of `b` for the extension. Remove this if `G2` is not supported. + + :::note + + All the params are not in Montgomery form. + + ::: + + :::note + + To convert number values into `storage` type you can use the following python function + +```python +import struct + +def unpack(x, field_size): + return ', '.join(["0x" + format(x, '08x') for x in struct.unpack('I' * (field_size) // 4, int(x).to_bytes(field_size, 'little'))]) +``` + +::: + +We also require some changes to [`curve_config.cuh`](https://github.com/ingonyama-zk/icicle/blob/main/icicle/curves/curve_config.cuh#L16-L29), we need to add a new curve id. + +``` +... + +#define BN254 1 +#define BLS12_381 2 +#define BLS12_377 3 +#define BW6_761 4 +#define GRUMPKIN 5 +#define 6 + +... +``` + +Make sure to modify the [rest of the file](https://github.com/ingonyama-zk/icicle/blob/4beda3a900eda961f39af3a496f8184c52bf3b41/icicle/curves/curve_config.cuh#L16-L29) accordingly. + +Finally we must modify the [`make` file](https://github.com/ingonyama-zk/icicle/blob/main/icicle/CMakeLists.txt#L64) to make sure we can compile our new curve. + +``` +set(SUPPORTED_CURVES bn254;bls12_381;bls12_377;bw6_761;grumpkin;) +``` + +### Adding Poseidon support + +If you want your curve to implement a Poseidon hash function or a tree builder, you will need to pre-calculate its optimized parameters. +Copy [constants_template.h](https://github.com/ingonyama-zk/icicle/blob/main/icicle/appUtils/poseidon/constants/constants_template.h) into `icicle/appUtils/poseidon/constants/_poseidon.h`. Run the [constants generation script](https://dev.ingonyama.com/icicle/primitives/poseidon#constants). The script will print the number of partial rounds and generate a `constants.bin` file. Use `xxd -i constants.bin` to parse the file into C declarations. Copy the `unsigned char constants_bin[]` contents inside your new file. Repeat this process for arities 2, 4, 8 and 11. + +After you've generated the constants, add your curve in this [SUPPORTED_CURVES_WITH_POSEIDON](https://github.com/ingonyama-zk/icicle/blob/main/icicle/CMakeLists.txt#L72) in the `CMakeLists.txt`. + +## Bindings + +In order to support a new curve in the binding libraries you first must support it in ICICLE core. + +### Rust + +Go to [rust curves folder](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/icicle-curves) and copy `icicle-curve-template` to a new folder named `icicle-`. + +Find all the occurrences of `` placeholder inside the crate. (You can use `Ctrl+Shift+F` in VS Code or `grep -nr ""` in bash). You will then need to replace each occurrence with your new curve name. + +#### Limbs + +Go to your curve's `curve.rs` file and set `SCALAR_LIMBS`, `BASE_LIMBS` and `G2_BASE_LIMBS` (if G2 is needed) to a minimum number of `u64` required to store a single scalar field / base field element respectively. +e.g. for bn254, scalar field is 254 bit so `SCALAR_LIMBS` is set to 4. + +#### Primitives + +If your curve doesn't support some of the primitives (ntt/msm/poseidon/merkle tree/), or you simply don't want to include it, just remove a corresponding module from `src` and then from `lib.rs` + +#### G2 + +If your curve doesn't support G2 - remove all the code under `#[cfg(feature = "g2")]` and remove the feature from [Cargo.toml](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-curves/icicle-bn254/Cargo.toml#L29) and [build.rs](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-curves/icicle-bn254/build.rs#L15). + +After this is done, add your new crate in the [global Cargo.toml](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/Cargo.toml). + +### Golang + +Golang is WIP in v1, coming soon. Please checkout a previous [release v0.1.0](https://github.com/ingonyama-zk/icicle/releases/tag/v0.1.0) for golang bindings. diff --git a/docs/versioned_docs/version-1.10.1/introduction.md b/docs/versioned_docs/version-1.10.1/introduction.md new file mode 100644 index 000000000..5a6368f1a --- /dev/null +++ b/docs/versioned_docs/version-1.10.1/introduction.md @@ -0,0 +1,47 @@ +--- +slug: / +displayed_sidebar: GettingStartedSidebar +title: '' +--- + +# Welcome to Ingonyama's Developer Documentation + +Ingonyama is a next-generation semiconductor company, focusing on Zero-Knowledge Proof hardware acceleration. We build accelerators for advanced cryptography, unlocking real-time applications. Our focus is on democratizing access to compute intensive cryptography and making it accessible for developers to build on top of. + +Currently our flagship products are: + +- **ICICLE**: + [ICICLE](https://github.com/ingonyama-zk/icicle) is a fully featured GPU accelerated cryptography library for building ZK provers. ICICLE allows you to accelerate your ZK existing protocols in a matter of hours or implement your protocol from scratch on GPU. + +--- + +## Our current take on hardware acceleration + +We believe GPUs are as important for ZK as for AI. + +- GPUs are a perfect match for ZK compute - around 97% of ZK protocol runtime is parallel by nature. +- GPUs are simple for developers to use and scale compared to other hardware platforms. +- GPUs are extremely competitive in terms of power / performance and price (3x cheaper compared to FPGAs). +- GPUs are popular and readily available. + +For a more in-depth understanding on this topic we suggest you read [our article on the subject](https://www.ingonyama.com/blog/revisiting-paradigm-hardware-acceleration-for-zero-knowledge-proofs). + +Despite our current focus on GPUs we are still hard at work developing a ZPU (ZK Processing Unit), with the goal of offering a programmable hardware platform for ZK. To read more about ZPUs we suggest you read this [article](https://medium.com/@ingonyama/zpu-the-zero-knowledge-processing-unit-f886a48e00e0). + +## ICICLE + +[ICICLE](https://github.com/ingonyama-zk/icicle) is a cryptography library for ZK using GPUs. +ICICLE implements blazing fast cryptographic primitives such as EC operations, MSM, NTT, Poseidon hash and more on GPU. + +ICICLE is designed to be easy to use, developers don't have to touch a single line of CUDA code. Our Rust and Golang bindings allow your team to transition from CPU to GPU with minimal changes. + +Learn more about ICICLE and GPUs [here][ICICLE-OVERVIEW]. + +## Get in Touch + +If you have any questions, ideas, or are thinking of building something in this space join the discussion on [Discord]. You can explore our code on [github](https://github.com/ingonyama-zk) or read some of [our research papers](https://github.com/ingonyama-zk/papers). + +Follow us on [Twitter](https://x.com/Ingo_zk) and [YouTube](https://www.youtube.com/@ingo_ZK) and sign up for our [mailing list](https://wkf.ms/3LKCbdj) to get our latest announcements. + +[ICICLE-OVERVIEW]: ./icicle/overview.md +[Discord]: https://discord.gg/6vYrE7waPj diff --git a/docs/versioned_docs/version-2.8.0/ZKContainers.md b/docs/versioned_docs/version-2.8.0/ZKContainers.md new file mode 100644 index 000000000..332c75cff --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/ZKContainers.md @@ -0,0 +1,12 @@ +# ZKContainer + +We found that developing ZK provers with ICICLE gives developers the ability to scale ZK provers across many machines and many GPUs. To make this possible we developed the ZKContainer. + +## What is a ZKContainer? + +A ZKContainer is a standardized, optimized and secure docker container that we configured with ICICLE applications in mind. A developer using our ZKContainer can deploy an ICICLE application on a single machine or on a thousand GPU machines in a data center with minimal concerns regarding compatibility. + +ZKContainer has been used by Ingonyama clients to achieve scalability across large data centers. +We suggest you read our [article](https://medium.com/@ingonyama/product-announcement-zk-containers-0e2a1f2d0a2b) regarding ZKContainer to understand the benefits of using them. + +![ZKContainer inside a ZK data center](/img/architecture-zkcontainer.png) diff --git a/docs/versioned_docs/version-2.8.0/contributor-guide.md b/docs/versioned_docs/version-2.8.0/contributor-guide.md new file mode 100644 index 000000000..ffd82cd9c --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/contributor-guide.md @@ -0,0 +1,23 @@ +# Contributor's Guide + +We welcome all contributions with open arms. At Ingonyama we take a village approach, believing it takes many hands and minds to build a ecosystem. + +## Contributing to ICICLE + +- Make suggestions or report bugs via [GitHub issues](https://github.com/ingonyama-zk/icicle/issues) +- Contribute to the ICICLE by opening a [pull request](https://github.com/ingonyama-zk/icicle/pulls). +- Contribute to our [documentation](https://github.com/ingonyama-zk/icicle/tree/main/docs) and [examples](https://github.com/ingonyama-zk/icicle/tree/main/examples). +- Ask questions on Discord + +### Opening a pull request + +When opening a [pull request](https://github.com/ingonyama-zk/icicle/pulls) please keep the following in mind. + +- `Clear Purpose` - The pull request should solve a single issue and be clean of any unrelated changes. +- `Clear description` - If the pull request is for a new feature describe what you built, why you added it and how its best that we test it. For bug fixes please describe the issue and the solution. +- `Consistent style` - Rust and Golang code should be linted by the official linters (golang fmt and rust fmt) and maintain a proper style. For CUDA and C++ code we use [`clang-format`](https://github.com/ingonyama-zk/icicle/blob/main/.clang-format), [here](https://github.com/ingonyama-zk/icicle/blob/605c25f9d22135c54ac49683b710fe2ce06e2300/.github/workflows/main-format.yml#L46) you can see how we run it. +- `Minimal Tests` - please add test which cover basic usage of your changes . + +## Questions? + +Find us on [Discord](https://discord.gg/6vYrE7waPj). diff --git a/docs/versioned_docs/version-2.8.0/grants.md b/docs/versioned_docs/version-2.8.0/grants.md new file mode 100644 index 000000000..1647d5400 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/grants.md @@ -0,0 +1,23 @@ +# Ingonyama Grant programs + +Ingonyama understands the importance of supporting and fostering a vibrant community of researchers and builders to advance ZK. To encourage progress, we are not only developing in the open but also sharing resources with researchers and builders through various programs. + +## ICICLE ZK-GPU Ecosystem Grant + +Ingonyama invites researchers and practitioners to collaborate in advancing ZK acceleration. We are allocating $100,000 for grants to support this initiative. + +### Bounties & Grants + +Eligibility for grants includes: + +1. **Students**: Utilize ICICLE in your research. +2. **Performance Improvement**: Enhance the performance of accelerated primitives in ICICLE. +3. **Protocol Porting**: Migrate existing ZK protocols to ICICLE. +4. **New Primitives**: Contribute new primitives to ICICLE. +5. **Benchmarking**: Compare ZK benchmarks against ICICLE. + +## Contact + +For questions or submissions: [grants@ingonyama.com](mailto:grants@ingonyama.com) + +**Read the full article [here](https://www.ingonyama.com/blog/icicle-for-researchers-grants-challenges)** diff --git a/docs/versioned_docs/version-2.8.0/icicle/colab-instructions.md b/docs/versioned_docs/version-2.8.0/icicle/colab-instructions.md new file mode 100644 index 000000000..d642dce4b --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/colab-instructions.md @@ -0,0 +1,138 @@ +# Run ICICLE on Google Colab + +Google Colab lets you use a GPU free of charge, it's an Nvidia T4 GPU with 16 GB of memory, capable of running latest CUDA (tested on Cuda 12.2) +As Colab is able to interact with shell commands, a user can also install a framework and load git repositories into Colab space. + +## Prepare Colab environment + +First thing to do in a notebook is to set the runtime type to a T4 GPU. + +- in the upper corner click on the dropdown menu and select "change runtime type" + +![Change runtime](/img/colab_change_runtime.png) + +- In the window select "T4 GPU" and press Save + +![T4 GPU](/img/t4_gpu.png) + +Installing Rust is rather simple, just execute the following command: + +```sh +!apt install rustc cargo +``` + +To test the installation of Rust: + +```sh +!rustc --version +!cargo --version +``` + +A successful installation will result in a rustc and cargo version print, a faulty installation will look like this: + +```sh +/bin/bash: line 1: rustc: command not found +/bin/bash: line 1: cargo: command not found +``` + +Now we will check the environment: + +```sh +!nvcc --version +!gcc --version +!cmake --version +!nvidia-smi +``` + +A correct environment should print the result with no bash errors for `nvidia-smi` command and result in a **Teslt T4 GPU** type: + +```sh +nvcc: NVIDIA (R) Cuda compiler driver +Copyright (c) 2005-2023 NVIDIA Corporation +Built on Tue_Aug_15_22:02:13_PDT_2023 +Cuda compilation tools, release 12.2, V12.2.140 +Build cuda_12.2.r12.2/compiler.33191640_0 +gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 +Copyright (C) 2021 Free Software Foundation, Inc. +This is free software; see the source for copying conditions. There is NO +warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. + +cmake version 3.27.9 + +CMake suite maintained and supported by Kitware (kitware.com/cmake). +Wed Jan 17 13:10:18 2024 ++---------------------------------------------------------------------------------------+ +| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 | +|-----------------------------------------+----------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+======================+======================| +| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 | +| N/A 39C P8 9W / 70W | 0MiB / 15360MiB | 0% Default | +| | | N/A | ++-----------------------------------------+----------------------+----------------------+ + ++---------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=======================================================================================| +| No running processes found | ++---------------------------------------------------------------------------------------+ +``` + +## Cloning ICICLE and running test + +Now we are ready to clone ICICE repository, + +```sh +!git clone https://github.com/ingonyama-zk/icicle.git +``` + +We now can browse the repository and run tests to check the runtime environment: + +```sh +!ls -la +%cd icicle +``` + +Let's run a test! +Navigate to icicle/wrappers/rust/icicle-curves/icicle-bn254 and run cargo test: + +```sh +%cd wrappers/rust/icicle-curves/icicle-bn254/ +!cargo test --release +``` + +:::note + +Compiling the first time may take a while + +::: + +Test run should end like this: + +```sh +running 15 tests +test curve::tests::test_ark_point_convert ... ok +test curve::tests::test_ark_scalar_convert ... ok +test curve::tests::test_affine_projective_convert ... ok +test curve::tests::test_point_equality ... ok +test curve::tests::test_field_convert_montgomery ... ok +test curve::tests::test_scalar_equality ... ok +test curve::tests::test_points_convert_montgomery ... ok +test msm::tests::test_msm ... ok +test msm::tests::test_msm_skewed_distributions ... ok +test ntt::tests::test_ntt ... ok +test ntt::tests::test_ntt_arbitrary_coset ... ok +test msm::tests::test_msm_batch has been running for over 60 seconds +test msm::tests::test_msm_batch ... ok +test ntt::tests::test_ntt_coset_from_subgroup ... ok +test ntt::tests::test_ntt_device_async ... ok +test ntt::tests::test_ntt_batch ... ok + +test result: ok. 15 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 99.39s +``` + +Viola, ICICLE in Colab! diff --git a/docs/versioned_docs/version-2.8.0/icicle/core.md b/docs/versioned_docs/version-2.8.0/icicle/core.md new file mode 100644 index 000000000..e887bae3b --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/core.md @@ -0,0 +1,196 @@ +# ICICLE Core + +ICICLE Core is a library written in C++/CUDA. All the ICICLE primitives are implemented within ICICLE Core. + +The Core is split into logical modules that can be compiled into static libraries using different [strategies](#compilation-strategies). You can then [link](#linking) these libraries with your C++ project or write your own [bindings](#writing-new-bindings-for-icicle) for other programming languages. If you want to use ICICLE with existing bindings please refer to the [Rust](/icicle/rust-bindings) or [Golang](/icicle/golang-bindings) bindings documentation. + +## Supported curves, fields and operations + +### Supported curves and operations + +| Operation\Curve | [bn254](https://neuromancer.sk/std/bn/bn254) | [bls12-377](https://neuromancer.sk/std/bls/BLS12-377) | [bls12-381](https://neuromancer.sk/std/bls/BLS12-381) | [bw6-761](https://eprint.iacr.org/2020/351) | grumpkin | +| --- | :---: | :---: | :---: | :---: | :---: | +| [MSM][MSM_DOCS] | ✅ | ✅ | ✅ | ✅ | ✅ | +| G2 | ✅ | ✅ | ✅ | ✅ | ❌ | +| [NTT][NTT_DOCS] | ✅ | ✅ | ✅ | ✅ | ❌ | +| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| [VecOps][VECOPS_CODE] | ✅ | ✅ | ✅ | ✅ | ✅ | +| [Polynomials][POLY_DOCS] | ✅ | ✅ | ✅ | ✅ | ❌ | +| [Poseidon](primitives/poseidon) | ✅ | ✅ | ✅ | ✅ | ✅ | +| [Merkle Tree](primitives/poseidon#the-tree-builder) | ✅ | ✅ | ✅ | ✅ | ✅ | + +### Supported fields and operations + +| Operation\Field | [babybear](https://eprint.iacr.org/2023/824.pdf) | [Stark252](https://docs.starknet.io/documentation/architecture_and_concepts/Cryptography/p-value/) | +| --- | :---: | :---: | +| [VecOps][VECOPS_CODE] | ✅ | ✅ | +| [Polynomials][POLY_DOCS] | ✅ | ✅ | +| [NTT][NTT_DOCS] | ✅ | ✅ | +| Extension Field | ✅ | ❌ | + +### Supported hashes + +| Hash | Sizes | +| --- | :---: | +| Keccak | 256, 512 | + +## Compilation strategies + +Most of the codebase is curve/field agnostic, which means it can be compiled for different curves and fields. When you build ICICLE Core you choose a single curve or field. If you need multiple curves or fields, you compile ICICLE once per curve or field that is needed. It's that simple. Currently, the following choices are supported: + +- [Field mode][COMPILE_FIELD_MODE] - used for STARK fields like BabyBear / Mersenne / Goldilocks. Includes field arithmetic, NTT, Poseidon, Extension fields and other primitives. +- [Curve mode][COMPILE_CURVE_MODE] - used for SNARK curves like BN254 / BLS curves / Grumpkin / etc. Curve mode is built upon field mode, so it includes everything that field does It also includes curve operations / MSM / ECNTT / G2 and other curve-related primitives. + +:::info + +If you only want to use a curve's scalar or base field, you still need to use curve mode. You can disable MSM with [options](#compilation-options) + +::: + +### Compiling for a field + +You can compile ICICLE for a field using this command: + +```sh +cd icicle +mkdir -p build +cmake -DFIELD= -S . -B build +cmake --build build -j +``` + +This command will output `libingo_field_.a` into `build/lib`. + +### Compiling for a curve + +:::note + +Field related primitives will be compiled for the scalar field of the curve + +::: + +You can compile ICICLE for a SNARK curve using this command: + +```sh +cd icicle +mkdir -p build +cmake -DCURVE= -S . -B build +cmake --build build -j +``` + +Where `` can be one of `bn254`/`bls12_377`/`bls12_381`/`bw6_761`/`grumpkin`. + +This command will output both `libingo_curve_.a` and `libingo_field_.a` into `build/lib`. + +### Compilation options + +There exist multiple options that allow you to customize your build or enable additional functionality. + +#### EXT_FIELD + +Used only in [field mode][COMPILE_FIELD_MODE] to add an Extension field. Adds all supported field operations for the extension field. + +Default: `OFF` + +Usage: `-DEXT_FIELD=ON` + +#### G2 + +Used only in [curve mode][COMPILE_CURVE_MODE] to add G2 definitions. Also adds G2 MSM. + +Default: `OFF` + +Usage: `-DG2=ON` + +#### ECNTT + +Used only in [curve mode][COMPILE_CURVE_MODE] to add ECNTT function. + +Default: `OFF` + +Usage: `-DECNTT=ON` + +#### MSM + +Used only in [curve mode][COMPILE_CURVE_MODE] to add MSM function. As MSM takes a lot of time to build, you can disable it with this option to reduce compilation time. + +Default: `ON` + +Usage: `-DMSM=OFF` + +#### BUILD_HASH + +Can be used in any mode to build a hash library. Currently it only includes Keccak hash function, but more are coming. + +Default: `OFF` + +Usage: `-DBUILD_HASH=ON` + +#### BUILD_TESTS + +Can be used in any mode to include tests runner binary. + +Default: `OFF` + +USAGE: `-DBUILD_TESTS=ON` + +#### BUILD_BENCHMARKS + +Can be used in any mode to include benchmarks runner binary. + +Default: `OFF` + +USAGE: `-DBUILD_BENCHMARKS=ON` + +#### DEVMODE + +Can be used in any mode to include debug symbols in the build. + +Default: `OFF` + +USAGE: `-DEVMODE=ON` + +## Linking + +To link ICICLE with your project you first need to compile ICICLE with options of your choice. After that you can use CMake `target_link_libraries` to link with the generated static libraries and `target_include_directories` to include ICICLE headers (located in `icicle/include`). + +Refer to our [c++ examples](https://github.com/ingonyama-zk/icicle/tree/main/examples/c%2B%2B) for more info. Take a look at this [CMakeLists.txt](https://github.com/ingonyama-zk/icicle/blob/main/examples/c%2B%2B/msm/CMakeLists.txt#L22) + +## Writing new bindings for ICICLE + +Since ICICLE Core is written in CUDA / C++ its really simple to generate static libraries. These static libraries can be installed on any system and called by higher level languages such as Golang. + +Static libraries can be loaded into memory once and used by multiple programs, reducing memory usage and potentially improving performance. They also allow you to separate functionality into distinct modules so your static library may need to compile only specific features that you want to use. + +Let's review the [Golang bindings][GOLANG_BINDINGS] since its a pretty verbose example (compared to rust which hides it pretty well) of using static libraries. Golang has a library named `CGO` which can be used to link static libraries. Here's a basic example on how you can use cgo to link these libraries: + +```go +/* +#cgo LDFLAGS: -L/path/to/shared/libs -lbn254 -lbls12_381 -lbls12_377 -lbw6_671 +#include "icicle.h" // make sure you use the correct header file(s) +*/ +import "C" + +func main() { + // Now you can call the C functions from the ICICLE libraries. + // Note that C function calls are prefixed with 'C.' in Go code. + + out := (*C.BN254_projective_t)(unsafe.Pointer(p)) + in := (*C.BN254_affine_t)(unsafe.Pointer(affine)) + + C.projective_from_affine_bn254(out, in) +} +``` + +The comments on the first line tell `CGO` which libraries to import as well as which header files to include. You can then call methods which are part of the static library and defined in the header file, `C.projective_from_affine_bn254` is an example. + +If you wish to create your own bindings for a language of your choice we suggest you start by investigating how you can call static libraries. + + +[GOLANG_BINDINGS]: golang-bindings.md +[COMPILE_CURVE_MODE]: #compiling-for-a-curve +[COMPILE_FIELD_MODE]: #compiling-for-a-field +[NTT_DOCS]: primitives/ntt +[MSM_DOCS]: primitives/msm +[POLY_DOCS]: polynomials/overview +[VECOPS_CODE]: https://github.com/ingonyama-zk/icicle/blob/main/icicle/include/vec_ops/vec_ops.cuh + diff --git a/docs/versioned_docs/version-2.8.0/icicle/golang-bindings.md b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings.md new file mode 100644 index 000000000..c2122b97f --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings.md @@ -0,0 +1,136 @@ +# Golang bindings + +Golang bindings allow you to use ICICLE as a golang library. +The source code for all Golang packages can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang). + +The Golang bindings are comprised of multiple packages. + +[`core`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/core) which defines all shared methods and structures, such as configuration structures, or memory slices. + +[`cuda-runtime`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/cuda_runtime) which defines abstractions for CUDA methods for allocating memory, initializing and managing streams, and `DeviceContext` which enables users to define and keep track of devices. + +Each supported curve, field, and hash has its own package which you can find in the respective directories [here](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang). If your project uses BN254 you only need to import that single package named [`bn254`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/curves/bn254). + +## Using ICICLE Golang bindings in your project + +To add ICICLE to your `go.mod` file. + +```bash +go get github.com/ingonyama-zk/icicle +``` + +If you want to specify a specific branch + +```bash +go get github.com/ingonyama-zk/icicle@ +``` + +For a specific commit + +```bash +go get github.com/ingonyama-zk/icicle@ +``` + +To build the shared libraries you can run [this](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/build.sh) script: + +```sh +./build.sh [-curve=] [-field=] [-hash=] [-cuda_version=] [-g2] [-ecntt] [-devmode] + +curve - The name of the curve to build or "all" to build all supported curves +field - The name of the field to build or "all" to build all supported fields +hash - The name of the hash to build or "all" to build all supported hashes +-g2 - Optional - build with G2 enabled +-ecntt - Optional - build with ECNTT enabled +-devmode - Optional - build in devmode +-help - Optional - Displays usage information +``` + +:::note + +If more than one curve or more than one field or more than one hash is supplied, the last one supplied will be built + +::: + +To build ICICLE libraries for all supported curves with G2 and ECNTT enabled. + +```bash +./build.sh -curve=all -g2 -ecntt +``` + +If you wish to build for a specific curve, for example bn254, without G2 or ECNTT enabled. + +``` bash +./build.sh -curve=bn254 +``` + +Now you can import ICICLE into your project + +```go +import ( + "github.com/stretchr/testify/assert" + "testing" + + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" +) +... +``` + +## Running tests + +To run all tests, for all curves: + +```bash +go test ./... -count=1 +``` + +If you wish to run test for a specific curve: + +```bash +go test -count=1 +``` + +## How do Golang bindings work? + +The libraries produced from the CUDA code compilation are used to bind Golang to ICICLE's CUDA code. + +1. These libraries (named `libingo_curve_.a` and `libingo_field_.a`) can be imported in your Go project to leverage the GPU accelerated functionalities provided by ICICLE. + +2. In your Go project, you can use `cgo` to link these libraries. Here's a basic example on how you can use `cgo` to link these libraries: + +```go +/* +#cgo LDFLAGS: -L/path/to/shared/libs -lingo_curve_bn254 -L$/path/to/shared/libs -lingo_field_bn254 -lstdc++ -lm +#include "icicle.h" // make sure you use the correct header file(s) +*/ +import "C" + +func main() { + // Now you can call the C functions from the ICICLE libraries. + // Note that C function calls are prefixed with 'C.' in Go code. +} +``` + +Replace `/path/to/shared/libs` with the actual path where the shared libraries are located on your system. + +## Supported curves, fields and operations + +### Supported curves and operations + +| Operation\Curve | bn254 | bls12_377 | bls12_381 | bw6-761 | grumpkin | +| --- | :---: | :---: | :---: | :---: | :---: | +| MSM | ✅ | ✅ | ✅ | ✅ | ✅ | +| G2 | ✅ | ✅ | ✅ | ✅ | ❌ | +| NTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| VecOps | ✅ | ✅ | ✅ | ✅ | ✅ | +| Polynomials | ✅ | ✅ | ✅ | ✅ | ❌ | + +### Supported fields and operations + +| Operation\Field | babybear | +| --- | :---: | +| VecOps | ✅ | +| Polynomials | ✅ | +| NTT | ✅ | +| Extension Field | ✅ | diff --git a/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/ecntt.md b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/ecntt.md new file mode 100644 index 000000000..3947995ed --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/ecntt.md @@ -0,0 +1,92 @@ +# ECNTT + +## ECNTT Method + +The `ECNtt[T any]()` function performs the Elliptic Curve Number Theoretic Transform (EC-NTT) on the input points slice, using the provided dir (direction), cfg (configuration), and stores the results in the results slice. + +```go +func ECNtt[T any](points core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) core.IcicleError +``` + +### Parameters + +- **`points`**: A slice of elliptic curve points (in projective coordinates) that will be transformed. The slice can be stored on the host or the device, as indicated by the `core.HostOrDeviceSlice` type. +- **`dir`**: The direction of the EC-NTT transform, either `core.KForward` or `core.KInverse`. +- **`cfg`**: A pointer to an `NTTConfig` object, containing configuration options for the NTT operation. +- **`results`**: A slice that will store the transformed elliptic curve points (in projective coordinates). The slice can be stored on the host or the device, as indicated by the `core.HostOrDeviceSlice` type. + +### Return Value + +- **`CudaError`**: A `core.IcicleError` value, which will be `core.IcicleErrorCode(0)` if the EC-NTT operation was successful, or an error if something went wrong. + +## NTT Configuration (NTTConfig) + +The `NTTConfig` structure holds configuration parameters for the NTT operation, allowing customization of its behavior to optimize performance based on the specifics of your protocol. + +```go +type NTTConfig[T any] struct { + Ctx cr.DeviceContext + CosetGen T + BatchSize int32 + ColumnsBatch bool + Ordering Ordering + areInputsOnDevice bool + areOutputsOnDevice bool + IsAsync bool + NttAlgorithm NttAlgorithm +} +``` + +### Fields + +- **`Ctx`**: Device context containing details like device ID and stream ID. +- **`CosetGen`**: Coset generator used for coset (i)NTTs, defaulting to no coset being used. +- **`BatchSize`**: The number of NTTs to compute in one operation, defaulting to 1. +- **`ColumnsBatch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. Defaults to `false`. +- **`Ordering`**: Ordering of inputs and outputs (`KNN`, `KNR`, `KRN`, `KRR`), affecting how data is arranged. +- **`areInputsOnDevice`**: Indicates if input scalars are located on the device. +- **`areOutputsOnDevice`**: Indicates if results are stored on the device. +- **`IsAsync`**: Controls whether the NTT operation runs asynchronously. +- **`NttAlgorithm`**: Explicitly select the NTT algorithm. ECNTT supports running on `Radix2` algoruithm. + +### Default Configuration + +Use `GetDefaultNTTConfig` to obtain a default configuration, customizable as needed. + +```go +func GetDefaultNTTConfig[T any](cosetGen T) NTTConfig[T] +``` + +## ECNTT Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" +) + +func Main() { + // Obtain the default NTT configuration with a predefined coset generator. + cfg := GetDefaultNttConfig() + + // Define the size of the input scalars. + size := 1 << 18 + + // Generate Points for the ECNTT operation. + points := GenerateProjectivePoints(size) + + // Set the direction of the NTT (forward or inverse). + dir := core.KForward + + // Allocate memory for the results of the NTT operation. + results := make(core.HostSlice[Projective], size) + + // Perform the NTT operation. + err := ECNtt(points, dir, &cfg, results) + if err != cr.CudaSuccess { + panic("ECNTT operation failed") + } +} +``` diff --git a/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/keccak.md b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/keccak.md new file mode 100644 index 000000000..1ecc80de1 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/keccak.md @@ -0,0 +1,94 @@ +# Keccak + +## Keccak Example + +```go +package main + +import ( + "encoding/hex" + + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/hash/keccak" +) + +func createHostSliceFromHexString(hexString string) core.HostSlice[uint8] { + byteArray, err := hex.DecodeString(hexString) + if err != nil { + panic("Not a hex string") + } + return core.HostSliceFromElements([]uint8(byteArray)) +} + +func main() { + input := createHostSliceFromHexString("1725b6") + outHost256 := make(core.HostSlice[uint8], 32) + + cfg := keccak.GetDefaultHashConfig() + e := keccak.Keccak256(input, int32(input.Len()), 1, outHost256, &cfg) + if e.CudaErrorCode != cr.CudaSuccess { + panic("Keccak256 hashing failed") + } + + outHost512 := make(core.HostSlice[uint8], 64) + e = keccak.Keccak512(input, int32(input.Len()), 1, outHost512, &cfg) + if e.CudaErrorCode != cr.CudaSuccess { + panic("Keccak512 hashing failed") + } + + numberOfBlocks := 3 + outHostBatch256 := make(core.HostSlice[uint8], 32*numberOfBlocks) + e = keccak.Keccak256(input, int32(input.Len()/numberOfBlocks), int32(numberOfBlocks), outHostBatch256, &cfg) + if e.CudaErrorCode != cr.CudaSuccess { + panic("Keccak256 batch hashing failed") + } +} +``` + +## Keccak Methods + +```go +func Keccak256(input core.HostOrDeviceSlice, inputBlockSize, numberOfBlocks int32, output core.HostOrDeviceSlice, config *HashConfig) core.IcicleError +func Keccak512(input core.HostOrDeviceSlice, inputBlockSize, numberOfBlocks int32, output core.HostOrDeviceSlice, config *HashConfig) core.IcicleError +``` + +### Parameters + +- **`input`**: A slice containing the input data for the Keccak256 hash function. It can reside in either host memory or device memory. +- **`inputBlockSize`**: An integer specifying the size of the input data for a single hash. +- **`numberOfBlocks`**: An integer specifying the number of results in the hash batch. +- **`output`**: A slice where the resulting hash will be stored. This slice can be in host or device memory. +- **`config`**: A pointer to a `HashConfig` object, which contains various configuration options for the Keccak256 operation. + +### Return Value + +- **`CudaError`**: Returns a CUDA error code indicating the success or failure of the Keccak256/Keccak512 operation. + +## HashConfig + +The `HashConfig` structure holds configuration parameters for the Keccak256/Keccak512 operation, allowing customization of its behavior to optimize performance based on the specifics of the operation or the underlying hardware. + +```go +type HashConfig struct { + Ctx cr.DeviceContext + areInputsOnDevice bool + areOutputsOnDevice bool + IsAsync bool +} +``` + +### Fields + +- **`Ctx`**: Device context containing details like device id and stream. +- **`areInputsOnDevice`**: Indicates if input data is located on the device. +- **`areOutputsOnDevice`**: Indicates if output hash is stored on the device. +- **`IsAsync`**: If true, runs the Keccak256/Keccak512 operation asynchronously. + +### Default Configuration + +Use `GetDefaultHashConfig` to obtain a default configuration, which can then be customized as needed. + +```go +func GetDefaultHashConfig() HashConfig +``` \ No newline at end of file diff --git a/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/msm-pre-computation.md b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/msm-pre-computation.md new file mode 100644 index 000000000..888a01dd5 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/msm-pre-computation.md @@ -0,0 +1,99 @@ +# MSM Pre computation + +To understand the theory behind MSM pre computation technique refer to Niall Emmart's [talk](https://youtu.be/KAWlySN7Hm8?feature=shared&t=1734). + +## Core package + +### MSM PrecomputePoints + +`PrecomputePoints` and `G2PrecomputePoints` exists for all supported curves. + +#### Description + +This function extends each provided base point $(P)$ with its multiples $(2^lP, 2^{2l}P, ..., 2^{(precompute_factor - 1) \cdot l}P)$, where $(l)$ is a level of precomputation determined by the `precompute_factor`. The extended set of points facilitates faster MSM computations by allowing the MSM algorithm to leverage precomputed multiples of base points, reducing the number of point additions required during the computation. + +The precomputation process is crucial for optimizing MSM operations, especially when dealing with large sets of points and scalars. By precomputing and storing multiples of the base points, the MSM function can more efficiently compute the scalar-point multiplications. + +#### `PrecomputePoints` + +Precomputes points for MSM by extending each base point with its multiples. + +```go +func PrecomputePoints(points core.HostOrDeviceSlice, msmSize int, cfg *core.MSMConfig, outputBases core.DeviceSlice) cr.CudaError +``` + +##### Parameters + +- **`points`**: A slice of the original affine points to be extended with their multiples. +- **`msmSize`**: The size of a single msm in order to determine optimal parameters. +- **`cfg`**: The MSM configuration parameters. +- **`outputBases`**: The device slice allocated for storing the extended points. + +##### Example + +```go +package main + +import ( + "log" + + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" +) + +func main() { + cfg := bn254.GetDefaultMSMConfig() + points := bn254.GenerateAffinePoints(1024) + var precomputeFactor int32 = 8 + var precomputeOut core.DeviceSlice + precomputeOut.Malloc(points[0].Size()*points.Len()*int(precomputeFactor), points[0].Size()) + + err := bn254.PrecomputePoints(points, 1024, &cfg, precomputeOut) + if err != cr.CudaSuccess { + log.Fatalf("PrecomputeBases failed: %v", err) + } +} +``` + +#### `G2PrecomputePoints` + +This method is the same as `PrecomputePoints` but for G2 points. Extends each G2 curve base point with its multiples for optimized MSM computations. + +```go +func G2PrecomputePoints(points core.HostOrDeviceSlice, msmSize int, cfg *core.MSMConfig, outputBases core.DeviceSlice) cr.CudaError +``` + +##### Parameters + +- **`points`**: A slice of the original affine points to be extended with their multiples. +- **`msmSize`**: The size of a single msm in order to determine optimal parameters. +- **`cfg`**: The MSM configuration parameters. +- **`outputBases`**: The device slice allocated for storing the extended points. + +##### Example + +```go +package main + +import ( + "log" + + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + g2 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/g2" +) + +func main() { + cfg := g2.G2GetDefaultMSMConfig() + points := g2.G2GenerateAffinePoints(1024) + var precomputeFactor int32 = 8 + var precomputeOut core.DeviceSlice + precomputeOut.Malloc(points[0].Size()*points.Len()*int(precomputeFactor), points[0].Size()) + + err := g2.G2PrecomputePoints(points, 1024, 0, &cfg, precomputeOut) + if err != cr.CudaSuccess { + log.Fatalf("PrecomputeBases failed: %v", err) + } +} +``` diff --git a/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/msm.md b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/msm.md new file mode 100644 index 000000000..72710c551 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/msm.md @@ -0,0 +1,198 @@ +# MSM + +## MSM Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + bn254_msm "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/msm" +) + +func main() { + // Obtain the default MSM configuration. + cfg := core.GetDefaultMSMConfig() + + // Define the size of the problem, here 2^18. + size := 1 << 18 + + // Generate scalars and points for the MSM operation. + scalars := bn254.GenerateScalars(size) + points := bn254.GenerateAffinePoints(size) + + // Create a CUDA stream for asynchronous operations. + stream, _ := cr.CreateStream() + var p bn254.Projective + + // Allocate memory on the device for the result of the MSM operation. + var out core.DeviceSlice + _, e := out.MallocAsync(p.Size(), p.Size(), stream) + + if e != cr.CudaSuccess { + panic(e) + } + + // Set the CUDA stream in the MSM configuration. + cfg.Ctx.Stream = &stream + cfg.IsAsync = true + + // Perform the MSM operation. + e = bn254_msm.Msm(scalars, points, &cfg, out) + + if e != cr.CudaSuccess { + panic(e) + } + + // Allocate host memory for the results and copy the results from the device. + outHost := make(core.HostSlice[bn254.Projective], 1) + cr.SynchronizeStream(&stream) + outHost.CopyFromDevice(&out) + + // Free the device memory allocated for the results. + out.Free() +} + +``` + +## MSM Method + +```go +func Msm(scalars core.HostOrDeviceSlice, points core.HostOrDeviceSlice, cfg *core.MSMConfig, results core.HostOrDeviceSlice) cr.CudaError +``` + +### Parameters + +- **`scalars`**: A slice containing the scalars for multiplication. It can reside either in host memory or device memory. +- **`points`**: A slice containing the points to be multiplied with scalars. Like scalars, these can also be in host or device memory. +- **`cfg`**: A pointer to an `MSMConfig` object, which contains various configuration options for the MSM operation. +- **`results`**: A slice where the results of the MSM operation will be stored. This slice can be in host or device memory. + +### Return Value + +- **`CudaError`**: Returns a CUDA error code indicating the success or failure of the MSM operation. + +## MSMConfig + +The `MSMConfig` structure holds configuration parameters for the MSM operation, allowing customization of its behavior to optimize performance based on the specifics of the operation or the underlying hardware. + +```go +type MSMConfig struct { + Ctx cr.DeviceContext + PrecomputeFactor int32 + C int32 + Bitsize int32 + LargeBucketFactor int32 + batchSize int32 + areScalarsOnDevice bool + AreScalarsMontgomeryForm bool + arePointsOnDevice bool + ArePointsMontgomeryForm bool + areResultsOnDevice bool + IsBigTriangle bool + IsAsync bool +} +``` + +### Fields + +- **`Ctx`**: Device context containing details like device id and stream. +- **`PrecomputeFactor`**: Controls the number of extra points to pre-compute. +- **`C`**: Window bitsize, a key parameter in the "bucket method" for MSM. +- **`Bitsize`**: Number of bits of the largest scalar. +- **`LargeBucketFactor`**: Sensitivity to frequently occurring buckets. +- **`batchSize`**: Number of results to compute in one batch. +- **`areScalarsOnDevice`**: Indicates if scalars are located on the device. +- **`AreScalarsMontgomeryForm`**: True if scalars are in Montgomery form. +- **`arePointsOnDevice`**: Indicates if points are located on the device. +- **`ArePointsMontgomeryForm`**: True if point coordinates are in Montgomery form. +- **`areResultsOnDevice`**: Indicates if results are stored on the device. +- **`IsBigTriangle`**: If `true` MSM will run in Large triangle accumulation if `false` Bucket accumulation will be chosen. Default value: false. +- **`IsAsync`**: If true, runs MSM asynchronously. + +### Default Configuration + +Use `GetDefaultMSMConfig` to obtain a default configuration, which can then be customized as needed. + +```go +func GetDefaultMSMConfig() MSMConfig +``` + +## How do I toggle between the supported algorithms? + +When creating your MSM Config you may state which algorithm you wish to use. `cfg.Ctx.IsBigTriangle = true` will activate Large triangle reduction and `cfg.Ctx.IsBigTriangle = false` will activate iterative reduction. + +```go +... + +// Obtain the default MSM configuration. +cfg := GetDefaultMSMConfig() + +cfg.Ctx.IsBigTriangle = true + +... +``` + +## How do I toggle between MSM modes? + +Toggling between MSM modes occurs automatically based on the number of results you are expecting from the `MSM` function. + +The number of results is interpreted from the size of `var out core.DeviceSlice`. Thus its important when allocating memory for `var out core.DeviceSlice` to make sure that you are allocating ` X `. + +```go +... + +batchSize := 3 +var p G2Projective +var out core.DeviceSlice +out.Malloc(batchSize*p.Size(), p.Size()) + +... +``` + +## Parameters for optimal performance + +Please refer to the [primitive description](../primitives/msm#choosing-optimal-parameters) + +## Support for G2 group + +To activate G2 support first you must make sure you are building the static libraries with G2 feature enabled as described in the [Golang building instructions](../golang-bindings.md#using-icicle-golang-bindings-in-your-project). + +Now you may import `g2` package of the specified curve. + +```go +import ( + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/g2" +) +``` + +This package include `G2Projective` and `G2Affine` points as well as a `G2Msm` method. + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + g2 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254/g2" +) + +func main() { + cfg := core.GetDefaultMSMConfig() + size := 1 << 12 + batchSize := 3 + totalSize := size * batchSize + scalars := bn254.GenerateScalars(totalSize) + points := g2.G2GenerateAffinePoints(totalSize) + + var p g2.G2Projective + var out core.DeviceSlice + out.Malloc(batchSize*p.Size(), p.Size()) + g2.G2Msm(scalars, points, &cfg, out) +} + +``` + +`G2Msm` works the same way as normal MSM, the difference is that it uses G2 Points. diff --git a/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/multi-gpu.md b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/multi-gpu.md new file mode 100644 index 000000000..3d7cdf187 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/multi-gpu.md @@ -0,0 +1,155 @@ +# Multi GPU APIs + +To learn more about the theory of Multi GPU programming refer to [this part](../multi-gpu.md) of documentation. + +Here we will cover the core multi GPU apis and an [example](#a-multi-gpu-example) + +## A Multi GPU example + +In this example we will display how you can + +1. Fetch the number of devices installed on a machine +2. For every GPU launch a thread and set an active device per thread. +3. Execute a MSM on each GPU + +```go +package main + +import ( + "fmt" + "sync" + + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" +) + +func main() { + numDevices, _ := cr.GetDeviceCount() + fmt.Println("There are ", numDevices, " devices available") + wg := sync.WaitGroup{} + + for i := 0; i < numDevices; i++ { + wg.Add(1) + // RunOnDevice makes sure each MSM runs on a single thread + cr.RunOnDevice(i, func(args ...any) { + defer wg.Done() + cfg := bn254.GetDefaultMSMConfig() + cfg.IsAsync = true + for _, power := range []int{10, 18} { + size := 1 << power // 2^pwr + + // generate random scalars + scalars := bn254.GenerateScalars(size) + points := bn254.GenerateAffinePoints(size) + + // create a stream and allocate result pointer + stream, _ := cr.CreateStream() + var p bn254.Projective + var out core.DeviceSlice + out.MallocAsync(p.Size(), p.Size(), stream) + // assign stream to device context + cfg.Ctx.Stream = &stream + + // execute MSM + bn254.Msm(scalars, points, &cfg, out) + // read result from device + outHost := make(core.HostSlice[bn254.Projective], 1) + outHost.CopyFromDeviceAsync(&out, stream) + out.FreeAsync(stream) + + // sync the stream + cr.SynchronizeStream(&stream) + } + }) + } + wg.Wait() +} +``` + +This example demonstrates a basic pattern for distributing tasks across multiple GPUs. The `RunOnDevice` function ensures that each goroutine is executed on its designated GPU and a corresponding thread. + +## Device Management API + +To streamline device management we offer as part of `cuda_runtime` package methods for dealing with devices. + +### `RunOnDevice` + +Runs a given function on a specific GPU device, ensuring that all CUDA calls within the function are executed on the selected device. + +In Go, most concurrency can be done via Goroutines. However, there is no guarantee that a goroutine stays on a specific host thread. + +`RunOnDevice` was designed to solve this caveat and ensure that the goroutine will stay on a specific host thread. + +`RunOnDevice` locks a goroutine into a specific host thread, sets a current GPU device, runs a provided function, and unlocks the goroutine from the host thread after the provided function finishes. + +While the goroutine is locked to the host thread, the Go runtime will not assign other goroutines to that host thread. + +**Parameters:** + +- **`deviceId int`**: The ID of the device on which to run the provided function. Device IDs start from 0. +- **`funcToRun func(args ...any)`**: The function to be executed on the specified device. +- **`args ...any`**: Arguments to be passed to `funcToRun`. + +**Behavior:** + +- The function `funcToRun` is executed in a new goroutine that is locked to a specific OS thread to ensure that all CUDA calls within the function target the specified device. + +:::note +Any goroutines launched within `funcToRun` are not automatically bound to the same GPU device. If necessary, `RunOnDevice` should be called again within such goroutines with the same `deviceId`. +::: + +**Example:** + +```go +RunOnDevice(0, func(args ...any) { + fmt.Println("This runs on GPU 0") + // CUDA-related operations here will target GPU 0 +}, nil) +``` + +### `SetDevice` + +Sets the active device for the current host thread. All subsequent CUDA calls made from this thread will target the specified device. + +:::warning +This function should not be used directly in conjunction with goroutines. If you want to run multi-gpu scenarios with goroutines you should use [RunOnDevice](#runondevice) +::: + +**Parameters:** + +- **`device int`**: The ID of the device to set as the current device. + +**Returns:** + +- **`CudaError`**: Error code indicating the success or failure of the operation. + +### `GetDeviceCount` + +Retrieves the number of CUDA-capable devices available on the host. + +**Returns:** + +- **`(int, CudaError)`**: The number of devices and an error code indicating the success or failure of the operation. + +### `GetDevice` + +Gets the ID of the currently active device for the calling host thread. + +**Returns:** + +- **`(int, CudaError)`**: The ID of the current device and an error code indicating the success or failure of the operation. + +### `GetDeviceFromPointer` + +Retrieves the device associated with a given pointer. + +**Parameters:** + +- **`ptr unsafe.Pointer`**: Pointer to query. + +**Returns:** + +- **`int`**: The device ID associated with the memory pointed to by `ptr`. + +This documentation should provide a clear understanding of how to effectively manage multiple GPUs in Go applications using CUDA, with a particular emphasis on the `RunOnDevice` function for executing tasks on specific GPUs. diff --git a/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/ntt.md b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/ntt.md new file mode 100644 index 000000000..b51c79f9a --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/ntt.md @@ -0,0 +1,151 @@ +# NTT + +## NTT Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" + + "github.com/consensys/gnark-crypto/ecc/bn254/fr/fft" +) + +func init() { + cfg := bn254.GetDefaultNttConfig() + initDomain(18, cfg) +} + +func initDomain[T any](largestTestSize int, cfg core.NTTConfig[T]) core.IcicleError { + rouMont, _ := fft.Generator(uint64(1 << largestTestSize)) + rou := rouMont.Bits() + rouIcicle := bn254.ScalarField{} + + rouIcicle.FromLimbs(rou[:]) + e := bn254.InitDomain(rouIcicle, cfg.Ctx, false) + return e +} + +func main() { + // Obtain the default NTT configuration with a predefined coset generator. + cfg := bn254.GetDefaultNttConfig() + + // Define the size of the input scalars. + size := 1 << 18 + + // Generate scalars for the NTT operation. + scalars := bn254.GenerateScalars(size) + + // Set the direction of the NTT (forward or inverse). + dir := core.KForward + + // Allocate memory for the results of the NTT operation. + results := make(core.HostSlice[bn254.ScalarField], size) + + // Perform the NTT operation. + err := bn254.Ntt(scalars, dir, &cfg, results) + if err.CudaErrorCode != cr.CudaSuccess { + panic("NTT operation failed") + } +} +``` + +## NTT Method + +```go +func Ntt[T any](scalars core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) core.IcicleError +``` + +### Parameters + +- **`scalars`**: A slice containing the input scalars for the transform. It can reside either in host memory or device memory. +- **`dir`**: The direction of the NTT operation (`KForward` or `KInverse`). +- **`cfg`**: A pointer to an `NTTConfig` object, containing configuration options for the NTT operation. +- **`results`**: A slice where the results of the NTT operation will be stored. This slice can be in host or device memory. + +### Return Value + +- **`CudaError`**: Returns a CUDA error code indicating the success or failure of the NTT operation. + +## NTT Configuration (NTTConfig) + +The `NTTConfig` structure holds configuration parameters for the NTT operation, allowing customization of its behavior to optimize performance based on the specifics of your protocol. + +```go +type NTTConfig[T any] struct { + Ctx cr.DeviceContext + CosetGen T + BatchSize int32 + ColumnsBatch bool + Ordering Ordering + areInputsOnDevice bool + areOutputsOnDevice bool + IsAsync bool + NttAlgorithm NttAlgorithm +} +``` + +### Fields + +- **`Ctx`**: Device context containing details like device ID and stream ID. +- **`CosetGen`**: Coset generator used for coset (i)NTTs, defaulting to no coset being used. +- **`BatchSize`**: The number of NTTs to compute in one operation, defaulting to 1. +- **`ColumnsBatch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. Defaults to `false`. +- **`Ordering`**: Ordering of inputs and outputs (`KNN`, `KNR`, `KRN`, `KRR`, `KMN`, `KNM`), affecting how data is arranged. +- **`areInputsOnDevice`**: Indicates if input scalars are located on the device. +- **`areOutputsOnDevice`**: Indicates if results are stored on the device. +- **`IsAsync`**: Controls whether the NTT operation runs asynchronously. +- **`NttAlgorithm`**: Explicitly select the NTT algorithm. Default value: Auto (the implementation selects radix-2 or mixed-radix algorithm based on heuristics). + +### Default Configuration + +Use `GetDefaultNTTConfig` to obtain a default configuration, customizable as needed. + +```go +func GetDefaultNTTConfig[T any](cosetGen T) NTTConfig[T] +``` + +### Initializing the NTT Domain + +Before performing NTT operations, it's necessary to initialize the NTT domain; it only needs to be called once per GPU since the twiddles are cached. + +```go +func InitDomain(primitiveRoot ScalarField, ctx cr.DeviceContext, fastTwiddles bool) core.IcicleError +``` + +This function initializes the domain with a given primitive root, optionally using fast twiddle factors to optimize the computation. + +### Releasing the domain + +The `ReleaseDomain` function is responsible for releasing the resources associated with a specific domain in the CUDA device context. + +```go +func ReleaseDomain(ctx cr.DeviceContext) core.IcicleError +``` + +### Parameters + +- **`ctx`**: a reference to the `DeviceContext` object, which represents the CUDA device context. + +### Return Value + +The function returns a `core.IcicleError`, which represents the result of the operation. If the operation is successful, the function returns `core.IcicleErrorCode(0)`. + +### Example + +```go +import ( + "github.com/icicle-crypto/icicle-core/cr" + "github.com/icicle-crypto/icicle-core/core" +) + +func example() { + cfg := GetDefaultNttConfig() + err := ReleaseDomain(cfg.Ctx) + if err != nil { + // Handle the error + } +} +``` diff --git a/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/vec-ops.md b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/vec-ops.md new file mode 100644 index 000000000..c39c8f2f0 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/golang-bindings/vec-ops.md @@ -0,0 +1,188 @@ +# Vector Operations + +## Overview + +Icicle exposes a number of vector operations which a user can use: + +* The VecOps API provides efficient vector operations such as addition, subtraction, and multiplication. +* MatrixTranspose API allows a user to perform a transpose on a vector representation of a matrix + +## VecOps API Documentation + +### Example + +#### Vector addition + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" +) + +func main() { + testSize := 1 << 12 + a := bn254.GenerateScalars(testSize) + b := bn254.GenerateScalars(testSize) + out := make(core.HostSlice[bn254.ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector multiplication + err := bn254.VecOp(a, b, out, cfg, core.Add) + if err != cr.CudaSuccess { + panic("Vector addition failed") + } +} +``` + +#### Vector Subtraction + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" +) + +func main() { + testSize := 1 << 12 + a := bn254.GenerateScalars(testSize) + b := bn254.GenerateScalars(testSize) + out := make(core.HostSlice[bn254.ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector multiplication + err := bn254.VecOp(a, b, out, cfg, core.Sub) + if err != cr.CudaSuccess { + panic("Vector subtraction failed") + } +} +``` + +#### Vector Multiplication + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v2/wrappers/golang/core" + cr "github.com/ingonyama-zk/icicle/v2/wrappers/golang/cuda_runtime" + bn254 "github.com/ingonyama-zk/icicle/v2/wrappers/golang/curves/bn254" +) + +func main() { + testSize := 1 << 12 + a := bn254.GenerateScalars(testSize) + b := bn254.GenerateScalars(testSize) + out := make(core.HostSlice[bn254.ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector multiplication + err := bn254.VecOp(a, b, out, cfg, core.Mul) + if err != cr.CudaSuccess { + panic("Vector multiplication failed") + } +} +``` + +### VecOps Method + +```go +func VecOp(a, b, out core.HostOrDeviceSlice, config core.VecOpsConfig, op core.VecOps) (ret cr.CudaError) +``` + +#### Parameters + +- **`a`**: The first input vector. +- **`b`**: The second input vector. +- **`out`**: The output vector where the result of the operation will be stored. +- **`config`**: A `VecOpsConfig` object containing various configuration options for the vector operations. +- **`op`**: The operation to perform, specified as one of the constants (`Sub`, `Add`, `Mul`) from the `VecOps` type. + +#### Return Value + +- **`CudaError`**: Returns a CUDA error code indicating the success or failure of the vector operation. + +### VecOpsConfig + +The `VecOpsConfig` structure holds configuration parameters for the vector operations, allowing customization of its behavior. + +```go +type VecOpsConfig struct { + Ctx cr.DeviceContext + isAOnDevice bool + isBOnDevice bool + isResultOnDevice bool + IsAsync bool +} +``` + +#### Fields + +- **Ctx**: Device context containing details like device ID and stream ID. +- **isAOnDevice**: Indicates if vector `a` is located on the device. +- **isBOnDevice**: Indicates if vector `b` is located on the device. +- **isResultOnDevice**: Specifies where the result vector should be stored (device or host memory). +- **IsAsync**: Controls whether the vector operation runs asynchronously. + +#### Default Configuration + +Use `DefaultVecOpsConfig` to obtain a default configuration, customizable as needed. + +```go +func DefaultVecOpsConfig() VecOpsConfig +``` + +## MatrixTranspose API Documentation + +This section describes the functionality of the `TransposeMatrix` function used for matrix transposition. + +The function takes a matrix represented as a 1D slice and transposes it, storing the result in another 1D slice. + +### Function + +```go +func TransposeMatrix(in, out core.HostOrDeviceSlice, columnSize, rowSize int, ctx cr.DeviceContext, onDevice, isAsync bool) (ret core.IcicleError) +``` + +## Parameters + +- **`in`**: The input matrix is a `core.HostOrDeviceSlice`, stored as a 1D slice. +- **`out`**: The output matrix is a `core.HostOrDeviceSlice`, which will be the transpose of the input matrix, stored as a 1D slice. +- **`columnSize`**: The number of columns in the input matrix. +- **`rowSize`**: The number of rows in the input matrix. +- **`ctx`**: The device context `cr.DeviceContext` to be used for the matrix transpose operation. +- **`onDevice`**: Indicates whether the input and output slices are stored on the device (GPU) or the host (CPU). +- **`isAsync`**: Indicates whether the matrix transpose operation should be executed asynchronously. + +## Return Value + +The function returns a `core.IcicleError` value, which represents the result of the matrix transpose operation. If the operation is successful, the returned value will be `0`. + +## Example Usage + +```go +var input = make(core.HostSlice[ScalarField], 20) +var output = make(core.HostSlice[ScalarField], 20) + +// Populate the input matrix +// ... + +// Get device context +ctx, _ := cr.GetDefaultDeviceContext() + +// Transpose the matrix +err := TransposeMatrix(input, output, 5, 4, ctx, false, false) +if err.IcicleErrorCode != core.IcicleErrorCode(0) { + // Handle the error +} + +// Use the transposed matrix +// ... +``` + +In this example, the `TransposeMatrix` function is used to transpose a 5x4 matrix stored in a 1D slice. The input and output slices are stored on the host (CPU), and the operation is executed synchronously. diff --git a/docs/versioned_docs/version-2.8.0/icicle/integrations.md b/docs/versioned_docs/version-2.8.0/icicle/integrations.md new file mode 100644 index 000000000..6fcf17e38 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/integrations.md @@ -0,0 +1,97 @@ +# ICICLE integrated provers + +ICICLE has been used by companies and projects such as [Celer Network](https://github.com/celer-network), [Consensys Gnark](https://github.com/Consensys/gnark), [EZKL](https://blog.ezkl.xyz/post/acceleration/), [ZKWASM](https://twitter.com/DelphinusLab/status/1762604988797513915) and others to accelerate their ZK proving pipeline. + +Many of these integrations have been a collaboration between Ingonyama and the integrating company. We have learned a lot about designing GPU based ZK provers. + +If you're interested in understanding these integrations better or learning how you can use ICICLE to accelerate your existing ZK proving pipeline this is the place for you. + +## A primer to building your own integrations + +Lets illustrate an ICICLE integration, so you can understand the core API and design overview of ICICLE. + +![ICICLE architecture](/img/architecture-high-level.png) + +Engineers usually use a cryptographic library to implement their ZK protocols. These libraries implement efficient primitives which are used as building blocks for the protocol; ICICLE is such a library. The difference is that ICICLE is designed from the start to run on GPUs; the Rust and Golang APIs abstract away all low level CUDA details. Our goal was to allow developers with no GPU experience to quickly get started with ICICLE. + +A developer may use ICICLE with two main approaches in mind. + +1. Drop-in replacement approach. +2. End-to-End GPU replacement approach. + +The first approach for GPU-accelerating your Prover with ICICLE is quick to implement, but it has limitations, such as reduced memory optimization and limited protocol tuning for GPUs. It's a solid starting point, but those committed to fully leveraging GPU acceleration should consider a more comprehensive approach. + +A End-to-End GPU replacement means performing the entire ZK proof on the GPU. This approach will reduce latency to a minimum and requires you to change the way you implement the protocol to be more GPU friendly. This approach will take full advantage of GPU acceleration. Redesigning your prover this way may take more engineering effort but we promise you that its worth it! + +## Using ICICLE integrated provers + +Here we cover how a developer can run existing circuits on ICICLE integrated provers. + +### Gnark + +[Gnark](https://github.com/Consensys/gnark) officially supports GPU proving with ICICLE. Currently only Groth16 on curve `BN254` is supported. This means that if you are currently using Gnark to write your circuits you can enjoy GPU acceleration without making many changes. + +:::info + +Currently ICICLE has been merged to Gnark [master branch](https://github.com/Consensys/gnark), however the [latest release](https://github.com/Consensys/gnark/releases/tag/v0.9.1) is from October 2023. + +::: + +Make sure your golang circuit project has `gnark` as a dependency and that you are using the master branch for now. + +``` +go get github.com/consensys/gnark@master +``` + +You should see two indirect dependencies added. + +``` +... + github.com/ingonyama-zk/icicle v0.1.0 // indirect + github.com/ingonyama-zk/iciclegnark v0.1.1 // indirect +... +``` + +:::info +As you may notice we are using ICICLE v0.1 here since golang bindings are only support in ICICLE v0.1 for the time being. +::: + +To switch over to ICICLE proving, make sure to change the backend you are using, below is an example of how this should be done. + +``` +// toggle on +proofIci, err := groth16.Prove(ccs, pk, secretWitness, backend.WithIcicleAcceleration()) + +// toggle off +proof, err := groth16.Prove(ccs, pk, secretWitness) +``` + +Now that you have enabled `WithIcicleAcceleration` backend simple change the way your run your circuits to: + +``` +go run -tags=icicle main.go +``` + +Your logs should look something like this if everything went as expected. + +``` +13:12:05 INF compiling circuit +13:12:05 INF parsed circuit inputs nbPublic=1 nbSecret=1 +13:12:05 INF building constraint builder nbConstraints=3 +13:12:05 DBG precomputing proving key in GPU acceleration=icicle backend=groth16 curve=bn254 nbConstraints=3 +13:12:05 DBG constraint system solver done nbConstraints=3 took=0.070259 +13:12:05 DBG prover done acceleration=icicle backend=groth16 curve=bn254 nbConstraints=3 took=80.356684 +13:12:05 DBG verifier done backend=groth16 curve=bn254 took=1.843888 +``` + +`acceleration=icicle` indicates that the prover is running in acceleration mode with ICICLE. + +You can reference the [Gnark docs](https://github.com/Consensys/gnark?tab=readme-ov-file#gpu-support) for further information. + +### Halo2 + +[Halo2](https://github.com/zkonduit/halo2) fork integrated with ICICLE for GPU acceleration. This means that you can run your existing Halo2 circuits with GPU acceleration just by activating a feature flag. + +To enable GPU acceleration just enable `icicle_gpu` [feature flag](https://github.com/zkonduit/halo2/blob/3d7b5e61b3052680ccb279e05bdcc21dd8a8fedf/halo2_proofs/Cargo.toml#L102). + +This feature flag will seamlessly toggle on GPU acceleration for you. diff --git a/docs/versioned_docs/version-2.8.0/icicle/introduction.md b/docs/versioned_docs/version-2.8.0/icicle/introduction.md new file mode 100644 index 000000000..998c38912 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/introduction.md @@ -0,0 +1,247 @@ +# Getting started with ICICLE + +This guide is oriented towards developers who want to start writing code with the ICICLE libraries. If you just want to run your existing ZK circuits on GPU refer to [this guide](./integrations.md#using-icicle-integrated-provers) please. + +## ICICLE repository overview + +![ICICLE API overview](/img/apilevels.png) + +The diagram above displays the general architecture of ICICLE and the API layers that exist. The CUDA API, which we also call ICICLE Core, is the lowest level and is comprised of CUDA kernels which implement all primitives such as MSM as well as C++ wrappers which expose these methods for different curves. + +ICICLE Core compiles into a static library. This library can be used with our official Golang and Rust wrappers or linked with your C++ project. You can also implement a wrapper for it in any other language. + +Based on this dependency architecture, the ICICLE repository has three main sections: + +- [ICICLE Core](#icicle-core) +- [ICICLE Rust bindings](#icicle-rust-and-golang-bindings) +- [ICICLE Golang bindings](#icicle-rust-and-golang-bindings) + +### ICICLE Core + +[ICICLE Core](./core.md) is a library that directly works with GPU by defining CUDA kernels and algorithms that invoke them. It contains code for [fast field arithmetic](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/field/field.cuh), cryptographic primitives used in ZK such as [NTT](https://github.com/ingonyama-zk/icicle/tree/main/icicle/src/ntt/), [MSM](https://github.com/ingonyama-zk/icicle/tree/main/icicle/src/msm/), [Poseidon Hash](https://github.com/ingonyama-zk/icicle/tree/main/icicle/src/poseidon/), [Polynomials](https://github.com/ingonyama-zk/icicle/tree/main/icicle/src/polynomials/) and others. + +ICICLE Core would typically be compiled into a static library and either used in a third party language such as Rust or Golang, or linked with your own C++ project. + +### ICICLE Rust and Golang bindings + +- [ICICLE Rust bindings](/icicle/rust-bindings) +- [ICICLE Golang bindings](/icicle/golang-bindings) + +These bindings allow you to easily use ICICLE in a Rust or Golang project. Setting up Golang bindings requires a bit of extra steps compared to the Rust bindings which utilize the `cargo build` tool. + +## Running ICICLE + +This guide assumes that you have a Linux or Windows machine with an Nvidia GPU installed. If you don't have access to an Nvidia GPU you can access one for free on [Google Colab](https://colab.google/). + +:::info note + +ICICLE can only run on Linux or Windows. **MacOS is not supported**. + +::: + +### Prerequisites + +- NVCC (version 12.0 or newer) +- cmake 3.18 and above +- GCC - version 9 or newer is recommended. +- Any Nvidia GPU +- Linux or Windows operating system. + +#### Optional Prerequisites + +- Docker, latest version. +- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/index.html) + +If you don't wish to install these prerequisites you can follow this tutorial using a [ZK-Container](https://github.com/ingonyama-zk/icicle/blob/main/Dockerfile) (docker container). To learn more about using ZK-Containers [read this](../ZKContainers.md). + +### Setting up ICICLE and running tests + +The objective of this guide is to make sure you can run the ICICLE Core, Rust and Golang tests. Achieving this will ensure you know how to setup ICICLE and run an ICICLE program. For simplicity, we will be using the ICICLE docker container as our environment, however, you may install the prerequisites on your machine and [skip](#icicle-core-1) the docker section. + +#### Setting up environment with Docker + +Lets begin by cloning the ICICLE repository: + +```sh +git clone https://github.com/ingonyama-zk/icicle +``` + +We will proceed to build the docker image [found here](https://github.com/ingonyama-zk/icicle/blob/main/Dockerfile): + +```sh +docker build -t icicle-demo . +docker run -it --runtime=nvidia --gpus all --name icicle_container icicle-demo +``` + +- `-it` runs the container in interactive mode with a terminal. +- `--gpus all` Allocate all available GPUs to the container. You can also specify which GPUs to use if you don't want to allocate all. +- `--runtime=nvidia` Use the NVIDIA runtime, necessary for GPU support. + +To read more about these settings reference this [article](https://developer.nvidia.com/nvidia-container-runtime). + +If you accidentally close your terminal and want to reconnect just call: + +```sh +docker exec -it icicle_container bash +``` + +Lets make sure that we have the correct CUDA version before proceeding + +```sh +nvcc --version +``` + +You should see something like this + +```sh +nvcc: NVIDIA (R) Cuda compiler driver +Copyright (c) 2005-2023 NVIDIA Corporation +Built on Tue_Aug_15_22:02:13_PDT_2023 +Cuda compilation tools, release 12.2, V12.2.140 +Build cuda_12.2.r12.2/compiler.33191640_0 +``` + +Make sure the release version is at least 12.0. + +#### ICICLE Core + +ICICLE Core is found under [`/icicle`](https://github.com/ingonyama-zk/icicle/tree/main/icicle). To build and run the tests first: + +```sh +cd icicle +``` + +For this example, we are going to compile ICICLE for a `bn254` curve. However other compilation strategies are supported. + +```sh +mkdir -p build +cmake -S . -B build -DCURVE=bn254 -DBUILD_TESTS=ON +cmake --build build -j +``` + +`-DBUILD_TESTS` option compiles the tests, without this flag `ctest` won't work. +`-DCURVE` option tells the compiler which curve to build. You can find a list of supported curves [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/cmake/CurvesCommon.cmake#L2). + +The output in `build` folder should include the static libraries for the compiled curve. + +To run the test + +```sh +cd build/tests +ctest +``` + +#### ICICLE Rust + +The rust bindings work by first compiling the CUDA static libraries as seen [here](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-curves/icicle-bn254/build.rs). The compilation of CUDA and the Rust library is all handled by the rust build toolchain. + +Similar to ICICLE Core here we also have to compile per curve. + +Lets compile curve `bn254` + +```sh +cd wrappers/rust/icicle-curves/icicle-bn254 +``` + +Now lets build our library + +```sh +cargo build --release +``` + +This may take a couple of minutes since we are compiling both the CUDA and Rust code. + +To run the tests + +```sh +cargo test +``` + +We also include some benchmarks + +```sh +cargo bench +``` + +#### ICICLE Golang + +The Golang bindings require compiling ICICLE Core first. We supply a [build script](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/golang/build.sh) to help build what you need. + +Script usage: + +```sh +./build.sh [-curve=] [-field=] [-hash=] [-cuda_version=] [-g2] [-ecntt] [-devmode] + +curve - The name of the curve to build or "all" to build all supported curves +field - The name of the field to build or "all" to build all supported fields +hash - The name of the hash to build or "all" to build all supported hashes +-g2 - Optional - build with G2 enabled +-ecntt - Optional - build with ECNTT enabled +-devmode - Optional - build in devmode +``` + +:::note + +If more than one curve or more than one field or more than one hash is supplied, the last one supplied will be built + +::: + +Once the library has been built, you can use and test the Golang bindings. + +To test a specific curve, field or hash, change to it's directory and then run: + +```sh +go test ./tests -count=1 -failfast -timeout 60m -p 2 -v +``` + +You will be able to see each test that runs, how long it takes and whether it passed or failed + +### Running ICICLE examples + +ICICLE examples can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/examples) these examples cover some simple use cases using C++, rust and golang. + +Lets run one of our C++ examples, in this case the [MSM example](https://github.com/ingonyama-zk/icicle/blob/main/examples/c%2B%2B/msm/example.cu). + +```sh +cd examples/c++/msm +./compile.sh +./run.sh +``` + +:::tip + +Read through the compile.sh and CMakeLists.txt to understand how to link your own C++ project with ICICLE + +::: + +#### Running with Docker + +In each example directory, ZK-container files are located in a subdirectory `.devcontainer`. + +```sh +msm/ +├── .devcontainer + ├── devcontainer.json + └── Dockerfile +``` + +Now lets build our docker file and run the test inside it. Make sure you have installed the [optional prerequisites](#optional-prerequisites). + +```sh +docker build -t icicle-example-msm -f .devcontainer/Dockerfile . +``` + +Lets start and enter the container + +```sh +docker run -it --rm --gpus all -v .:/icicle-example icicle-example-msm +``` + +Inside the container you can run the same commands: + +```sh +./compile.sh +./run.sh +``` + +You can now experiment with our other examples, perhaps try to run a rust or golang example next. diff --git a/docs/versioned_docs/version-2.8.0/icicle/multi-gpu.md b/docs/versioned_docs/version-2.8.0/icicle/multi-gpu.md new file mode 100644 index 000000000..3816ddd36 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/multi-gpu.md @@ -0,0 +1,61 @@ +# Multi GPU with ICICLE + +:::info + +If you are looking for the Multi GPU API documentation refer [here](./rust-bindings/multi-gpu.md) for Rust and [here](./golang-bindings/multi-gpu.md) for Golang. + +::: + +One common challenge with Zero-Knowledge computation is managing the large input sizes. It's not uncommon to encounter circuits surpassing 2^25 constraints, pushing the capabilities of even advanced GPUs to their limits. To effectively scale and process such large circuits, leveraging multiple GPUs in tandem becomes a necessity. + +Multi-GPU programming involves developing software to operate across multiple GPU devices. Lets first explore different approaches to Multi-GPU programming then we will cover how ICICLE allows you to easily develop youR ZK computations to run across many GPUs. + +## Approaches to Multi GPU programming + +There are many [different strategies](https://github.com/NVIDIA/multi-gpu-programming-models) available for implementing multi GPU, however, it can be split into two categories. + +### GPU Server approach + +This approach usually involves a single or multiple CPUs opening threads to read / write from multiple GPUs. You can think about it as a scaled up HOST - Device model. + +![Multi-GPU Server Approach](/img/image.png) + +This approach won't let us tackle larger computation sizes but it will allow us to compute multiple computations which we wouldn't be able to load onto a single GPU. + +For example let's say that you had to compute two MSMs of size 2^26 on a 16GB VRAM GPU you would normally have to perform them asynchronously. However, if you double the number of GPUs in your system you can now run them in parallel. + +### Inter GPU approach + +This approach involves a more sophisticated approach to multi GPU computation. Using technologies such as [GPUDirect, NCCL, NVSHMEM](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-cwes1084/) and NVLink it's possible to combine multiple GPUs and split a computation among different devices. + +This approach requires redesigning the algorithm at the software level to be compatible with splitting amongst devices. In some cases, to lower latency to a minimum, special inter GPU connections would be installed on a server to allow direct communication between multiple GPUs. + +## Writing ICICLE Code for Multi GPUs + +The approach we have taken for the moment is a GPU Server approach; we assume you have a machine with multiple GPUs and you wish to run some computation on each GPU. + +To dive deeper and learn about the API check out the docs for our different ICICLE API + +- [Rust Multi GPU APIs](./rust-bindings/multi-gpu.md) +- [Golang Multi GPU APIs](./golang-bindings/multi-gpu.md) +- C++ Multi GPU APIs + +## Best practices + +- Never hardcode device IDs, if you want your software to take advantage of all GPUs on a machine use methods such as `get_device_count` to support arbitrary number of GPUs. + +- Launch one CPU thread per GPU. To avoid [nasty errors](https://developer.nvidia.com/blog/cuda-pro-tip-always-set-current-device-avoid-multithreading-bugs/) and hard to read code we suggest that for every GPU you create a dedicated thread. Within a CPU thread you should be able to launch as many tasks as you wish for a GPU as long as they all run on the same GPU id. This will make your code way more manageable, easy to read and performant. + +## ZKContainer support for multi GPUs + +Multi GPU support should work with ZK-Containers by simply defining which devices the docker container should interact with: + +```sh +docker run -it --gpus '"device=0,2"' zk-container-image +``` + +If you wish to expose all GPUs + +```sh +docker run --gpus all zk-container-image +``` diff --git a/docs/versioned_docs/version-2.8.0/icicle/overview.md b/docs/versioned_docs/version-2.8.0/icicle/overview.md new file mode 100644 index 000000000..36e19ede0 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/overview.md @@ -0,0 +1,56 @@ +# What is ICICLE? + +[ICICLE](https://github.com/ingonyama-zk/icicle) is a cryptography library for ZK using GPUs. ICICLE implements blazing fast cryptographic primitives such as EC operations, MSM, NTT, Poseidon hash and more on GPU. + +ICICLE allows developers with minimal GPU experience to effortlessly accelerate their ZK application; from our experiments, even the most naive implementation may yield 10X improvement in proving times. + +ICICLE has been used by many leading ZK companies such as [Celer Network](https://github.com/celer-network), [Gnark](https://github.com/Consensys/gnark) and others to accelerate their ZK proving pipeline. + +## Dont have access to a GPU? + +We understand that not all developers have access to a GPU and we don't want this to limit anyone from developing with ICICLE. +Here are some ways we can help you gain access to GPUs: + +:::note + +If none of the following options suit your needs, contact us on [telegram](https://t.me/RealElan) for assistance. We're committed to ensuring that a lack of a GPU doesn't become a bottleneck for you. If you need help with setup or any other issues, we're here to help you. + +::: + +### Grants + +At Ingonyama we are interested in accelerating the progress of ZK and cryptography. If you are an engineer, developer or an academic researcher we invite you to checkout [our grant program](https://www.ingonyama.com/blog/icicle-for-researchers-grants-challenges). We will give you access to GPUs and even pay you to do your dream research! + +### Google Colab + +This is a great way to get started with ICICLE instantly. Google Colab offers free GPU access to a NVIDIA T4 instance with 16 GB of memory which should be enough for experimenting and even prototyping with ICICLE. + +For an extensive guide on how to setup Google Colab with ICICLE refer to [this article](./colab-instructions.md). + +### Vast.ai + +[Vast.ai](https://vast.ai/) is a global GPU marketplace where you can rent many different types of GPUs by the hour for [competitive pricing](https://vast.ai/pricing). They provide on-demand and interruptible rentals depending on your need or use case; you can learn more about their rental types [here](https://vast.ai/faq#rental-types). + +## What can you do with ICICLE? + +[ICICLE](https://github.com/ingonyama-zk/icicle) can be used in the same way you would use any other cryptography library. While developing and integrating ICICLE into many proof systems, we found some use case categories: + +### Circuit developers + +If you are a circuit developer and are experiencing bottlenecks while running your circuits, an ICICLE integrated prover may be the solution. + +ICICLE has been integrated into a number of popular ZK provers including [Gnark prover](https://github.com/Consensys/gnark) and [Halo2](https://github.com/zkonduit/halo2). This means that you can enjoy GPU acceleration for your existing circuits immediately without writing a single line of code by simply switching on the GPU prover flag! + +### Integrating into existing ZK provers + +From our collaborations we have learned that its possible to accelerate a specific part of your prover to solve for a specific bottleneck. + +ICICLE can be used to accelerate specific parts of your prover without completely rewriting your ZK prover. + +### Developing your own ZK provers + +If your goal is to build a ZK prover from the ground up, ICICLE is an ideal tool for creating a highly optimized and scalable ZK prover. A key benefit of using GPUs with ICICLE is the ability to scale your ZK prover efficiently across multiple machines within a data center. + +### Developing proof of concepts + +ICICLE is also ideal for developing small prototypes. ICICLE has Golang and Rust bindings so you can easily develop a library implementing a specific primitive using ICICLE. An example would be develop a KZG commitment library using ICICLE. diff --git a/docs/versioned_docs/version-2.8.0/icicle/polynomials/ffi.uml b/docs/versioned_docs/version-2.8.0/icicle/polynomials/ffi.uml new file mode 100644 index 000000000..5d0a1e3c8 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/polynomials/ffi.uml @@ -0,0 +1,27 @@ +@startuml +skinparam componentStyle uml2 + +' Define Components +component "C++ Template\nComponent" as CppTemplate { + [Parameterizable Interface] +} +component "C API Wrapper\nComponent" as CApiWrapper { + [C API Interface] +} +component "Rust Code\nComponent" as RustCode { + [Macro Interface\n(Template Instantiation)] +} + +' Define Artifact +artifact "Static Library\n«artifact»" as StaticLib + +' Connections +CppTemplate -down-> CApiWrapper : Instantiates +CApiWrapper .down.> StaticLib : Compiles into +RustCode -left-> StaticLib : Links against\nand calls via FFI + +' Notes +note right of CppTemplate : Generic C++\ntemplate implementation +note right of CApiWrapper : Exposes C API for FFI\nto Rust/Go +note right of RustCode : Uses macros to\ninstantiate templates +@enduml diff --git a/docs/versioned_docs/version-2.8.0/icicle/polynomials/hw_backends.uml b/docs/versioned_docs/version-2.8.0/icicle/polynomials/hw_backends.uml new file mode 100644 index 000000000..bb96db092 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/polynomials/hw_backends.uml @@ -0,0 +1,86 @@ +@startuml + +' Define Interface for Polynomial Backend Operations +interface IPolynomialBackend { + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +' Define Interface for Polynomial Context (State Management) +interface IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +' PolynomialAPI now uses two strategies: Backend and Context +class PolynomialAPI { + -backendStrategy: IPolynomialBackend + -contextStrategy: IPolynomialContext + -setBackendStrategy(IPolynomialBackend) + -setContextStrategy(IPolynomialContext) + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +' Backend Implementations +class GPUPolynomialBackend implements IPolynomialBackend { + #gpuResources: Resource + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +class ZPUPolynomialBackend implements IPolynomialBackend { + #zpuResources: Resource + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +class TracerPolynomialBackend implements IPolynomialBackend { + #traceData: Data + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +' Context Implementations (Placeholder for actual implementation) +class GPUContext implements IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +class ZPUContext implements IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +class TracerContext implements IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +' Relationships +PolynomialAPI o-- IPolynomialBackend : uses +PolynomialAPI o-- IPolynomialContext : uses +@enduml diff --git a/docs/versioned_docs/version-2.8.0/icicle/polynomials/overview.md b/docs/versioned_docs/version-2.8.0/icicle/polynomials/overview.md new file mode 100644 index 000000000..06058ffe4 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/polynomials/overview.md @@ -0,0 +1,388 @@ +# Polynomial API Overview + +:::note +Read our paper on the Polynomials API in ICICLE v2 by clicking [here](https://eprint.iacr.org/2024/973). +::: + +## Introduction + +The Polynomial API offers a robust framework for polynomial operations within a computational environment. It's designed for flexibility and efficiency, supporting a broad range of operations like arithmetic, evaluation, and manipulation, all while abstracting from the computation and storage specifics. This enables adaptability to various backend technologies, employing modern C++ practices. + +## Key Features + +### Backend Agnostic Architecture + +Our API is structured to be independent of any specific computational backend. While a CUDA backend is currently implemented, the architecture facilitates easy integration of additional backends. This capability allows users to perform polynomial operations without the need to tailor their code to specific hardware, enhancing code portability and scalability. + +### Templating in the Polynomial API + +The Polynomial API is designed with a templated structure to accommodate different data types for coefficients, the domain, and images. This flexibility allows the API to be adapted for various computational needs and types of data. + +```cpp +template +class Polynomial { + // Polynomial class definition +} +``` + +In this template: + +- **`Coeff`**: Represents the type of the coefficients of the polynomial. +- **`Domain`**: Specifies the type for the input values over which the polynomial is evaluated. By default, it is the same as the type of the coefficients but can be specified separately to accommodate different computational contexts. +- **`Image`**: Defines the type of the output values of the polynomial. This is typically the same as the coefficients. + +#### Default instantiation + +```cpp +extern template class Polynomial; +``` + +#### Extended use cases + +The templated nature of the Polynomial API also supports more complex scenarios. For example, coefficients and images could be points on an elliptic curve (EC points), which are useful in cryptographic applications and advanced algebraic structures. This approach allows the API to be extended easily to support new algebraic constructions without modifying the core implementation. + +### Supported Operations + +The Polynomial class encapsulates a polynomial, providing a variety of operations: + +- **Construction**: Create polynomials from coefficients or evaluations on roots-of-unity domains. +- **Arithmetic Operations**: Perform addition, subtraction, multiplication, and division. +- **Evaluation**: Directly evaluate polynomials at specific points or across a domain. +- **Manipulation**: Features like slicing polynomials, adding or subtracting monomials inplace, and computing polynomial degrees. +- **Memory Access**: Access internal states or obtain device-memory views of polynomials. + +## Usage + +This section outlines how to use the Polynomial API in C++. Bindings for Rust and Go are detailed under the Bindings sections. + +### Backend Initialization + +Initialization with an appropriate factory is required to configure the computational context and backend. + +```cpp +#include "polynomials/polynomials.h" +#include "polynomials/cuda_backend/polynomial_cuda_backend.cuh" + +// Initialize with a CUDA backend +Polynomial::initialize(std::make_shared()); +``` + +:::note +Initialization of a factory must be done per linked curve or field. +::: + +### Construction + +Polynomials can be constructed from coefficients, from evaluations on roots-of-unity domains, or by cloning existing polynomials. + +```cpp +// Construction +static Polynomial from_coefficients(const Coeff* coefficients, uint64_t nof_coefficients); +static Polynomial from_rou_evaluations(const Image* evaluations, uint64_t nof_evaluations); +// Clone the polynomial +Polynomial clone() const; +``` + +Example: + +```cpp +auto p_from_coeffs = Polynomial_t::from_coefficients(coeff /* :scalar_t* */, nof_coeffs); +auto p_from_rou_evals = Polynomial_t::from_rou_evaluations(rou_evals /* :scalar_t* */, nof_evals); +auto p_cloned = p.clone(); // p_cloned and p do not share memory +``` + +:::note +The coefficients or evaluations may be allocated either on host or device memory. In both cases the memory is copied to the backend device. +::: + +### Arithmetic + +Constructed polynomials can be used for various arithmetic operations: + +```cpp +// Addition +Polynomial operator+(const Polynomial& rhs) const; +Polynomial& operator+=(const Polynomial& rhs); // inplace addition + +// Subtraction +Polynomial operator-(const Polynomial& rhs) const; + +// Multiplication +Polynomial operator*(const Polynomial& rhs) const; +Polynomial operator*(const Domain& scalar) const; // scalar multiplication + +// Division A(x) = B(x)Q(x) + R(x) +std::pair divide(const Polynomial& rhs) const; // returns (Q(x), R(x)) +Polynomial operator/(const Polynomial& rhs) const; // returns quotient Q(x) +Polynomial operator%(const Polynomial& rhs) const; // returns remainder R(x) +Polynomial divide_by_vanishing_polynomial(uint64_t degree) const; // sdivision by the vanishing polynomial V(x)=X^N-1 +``` + +#### Example + +Given polynomials A(x),B(x),C(x) and V(x) the vanishing polynomial. + +$$ +H(x)=\frac{A(x) \cdot B(x) - C(x)}{V(x)} \space where \space V(x) = X^{N}-1 +$$ + +```cpp +auto H = (A*B-C).divide_by_vanishing_polynomial(N); +``` + +### Evaluation + +Evaluate polynomials at arbitrary domain points, across a domain or on a roots-of-unity domain. + +```cpp +Image operator()(const Domain& x) const; // evaluate f(x) +void evaluate(const Domain* x, Image* evals /*OUT*/) const; +void evaluate_on_domain(Domain* domain, uint64_t size, Image* evals /*OUT*/) const; // caller allocates memory +void evaluate_on_rou_domain(uint64_t domain_log_size, Image* evals /*OUT*/) const; // caller allocate memory +``` + +Example: + +```cpp +Coeff x = rand(); +Image f_x = f(x); // evaluate f at x + +// evaluate f(x) on a domain +uint64_t domain_size = ...; +auto domain = /*build domain*/; // host or device memory +auto evaluations = std::make_unique(domain_size); // can be device memory too +f.evaluate_on_domain(domain, domain_size, evaluations); + +// evaluate f(x) on roots of unity domain +uint64_t domain_log_size = ...; +auto evaluations_rou_domain = std::make_unique(1 << domain_log_size); // can be device memory too +f.evaluate_on_rou_domain(domain_log_size, evaluations_rou_domain); +``` + +### Manipulations + +Beyond arithmetic, the API supports efficient polynomial manipulations: + +#### Monomials + +```cpp +// Monomial operations +Polynomial& add_monomial_inplace(Coeff monomial_coeff, uint64_t monomial = 0); +Polynomial& sub_monomial_inplace(Coeff monomial_coeff, uint64_t monomial = 0); +``` + +The ability to add or subtract monomials directly and in-place is an efficient way to manipualte polynomials. + +Example: + +```cpp +f.add_monomial_in_place(scalar_t::from(5)); // f(x) += 5 +f.sub_monomial_in_place(scalar_t::from(3), 8); // f(x) -= 3x^8 +``` + +#### Computing the degree of a Polynomial + +```cpp +// Degree computation +int64_t degree(); +``` + +The degree of a polynomial is a fundamental characteristic that describes the highest power of the variable in the polynomial expression with a non-zero coefficient. +The `degree()` function in the API returns the degree of the polynomial, corresponding to the highest exponent with a non-zero coefficient. + +- For the polynomial $f(x) = x^5 + 2x^3 + 4$, the degree is 5 because the highest power of $x$ with a non-zero coefficient is 5. +- For a scalar value such as a constant term (e.g., $f(x) = 7$, the degree is considered 0, as it corresponds to $x^0$. +- The degree of the zero polynomial, $f(x) = 0$, where there are no non-zero coefficients, is defined as -1. This special case often represents an "empty" or undefined state in many mathematical contexts. + +Example: + +```cpp +auto f = /*some expression*/; +auto degree_of_f = f.degree(); +``` + +#### Slicing + +```cpp +// Slicing and selecting even or odd components. +Polynomial slice(uint64_t offset, uint64_t stride, uint64_t size = 0 /*0 means take all elements*/); +Polynomial even(); +Polynomial odd(); +``` + +The Polynomial API provides methods for slicing polynomials and selecting specific components, such as even or odd indexed terms. Slicing allows extracting specific sections of a polynomial based on an offset, stride, and size. + +The following examples demonstrate folding a polynomial's even and odd parts and arbitrary slicing; + +```cpp +// folding a polynomials even and odd parts with randomness +auto x = rand(); +auto even = f.even(); +auto odd = f.odd(); +auto fold_poly = even + odd * x; + +// arbitrary slicing (first quarter) +auto first_quarter = f.slice(0 /*offset*/, 1 /*stride*/, f.degree()/4 /*size*/); +``` + +### Memory access (copy/view) + +Access to the polynomial's internal state can be vital for operations like commitment schemes or when more efficient custom operations are necessary. This can be done either by copying or viewing the polynomial + +#### Copying + +Copies the polynomial coefficients to either host or device allocated memory. + +:::note +Copying to host memory is backend agnostic while copying to device memory requires the memory to be allocated on the corresponding backend. +::: + +```cpp +Coeff get_coeff(uint64_t idx) const; // copy single coefficient to host +uint64_t copy_coeffs(Coeff* coeffs, uint64_t start_idx, uint64_t end_idx) const; +``` + +Example: + +```cpp +auto coeffs_device = /*allocate CUDA or host memory*/ +f.copy_coeffs(coeffs_device, 0/*start*/, f.degree()); + +MSMConfig cfg = msm::defaultMSMConfig(); +cfg.are_points_on_device = true; // assuming copy to device memory +auto rv = msm::MSM(coeffs_device, points, msm_size, cfg, results); +``` + +#### Views + +The Polynomial API supports efficient data handling through the use of memory views. These views provide direct access to the polynomial's internal state without the need to copy data. This feature is particularly useful for operations that require direct access to device memory, enhancing both performance and memory efficiency. + +##### What is a Memory View? + +A memory view is essentially a pointer to data stored in device memory. By providing a direct access pathway to the data, it eliminates the need for data duplication, thus conserving both time and system resources. This is especially beneficial in high-performance computing environments where data size and operation speed are critical factors. + +##### Applications of Memory Views + +Memory views are extremely versatile and can be employed in various computational contexts such as: + +- **Commitments**: Views can be used to commit polynomial states in cryptographic schemes, such as Multi-Scalar Multiplications (MSM). +- **External Computations**: They allow external functions or algorithms to utilize the polynomial's data directly, facilitating operations outside the core polynomial API. This is useful for custom operations that are not covered by the API. + +##### Obtaining and Using Views + +To create and use views within the Polynomial API, functions are provided to obtain pointers to both coefficients and evaluation data. Here’s how they are generally structured: + +```cpp +// Obtain a view of the polynomial's coefficients +std::tuple, uint64_t /*size*/, uint64_t /*device_id*/> get_coefficients_view(); +``` + +Example usage: + +```cpp +auto [coeffs_view, size, device_id] = polynomial.get_coefficients_view(); + +// Use coeffs_view in a computational routine that requires direct access to polynomial coefficients +// Example: Passing the view to a GPU-accelerated function +gpu_accelerated_function(coeffs_view.get(),...); +``` + +##### Integrity-Pointer: Managing Memory Views + +Within the Polynomial API, memory views are managed through a specialized tool called the Integrity-Pointer. This pointer type is designed to safeguard operations by monitoring the validity of the memory it points to. It can detect if the memory has been modified or released, thereby preventing unsafe access to stale or non-existent data. +The Integrity-Pointer not only acts as a regular pointer but also provides additional functionality to ensure the integrity of the data it references. Here are its key features: + +```cpp +// Checks whether the pointer is still considered valid +bool isValid() const; + +// Retrieves the raw pointer or nullptr if pointer is invalid +const T* get() const; + +// Dereferences the pointer. Throws exception if the pointer is invalid. +const T& operator*() const; + +//Provides access to the member of the pointed-to object. Throws exception if the pointer is invalid. +const T* operator->() const; +``` + +Consider the Following case: + +```cpp +auto [coeff_view, size, device] = f.get_coefficients_view(); + +// Use the coefficients view to perform external operations +commit_to_polynomial(coeff_view.get(), size); + +// Modification of the original polynomial +f += g; // Any operation that modifies 'f' potentially invalidates 'coeff_view' + +// Check if the view is still valid before using it further +if (coeff_view.isValid()) { + perform_additional_computation(coeff_view.get(), size); +} else { + handle_invalid_data(); +} +``` + + + +## Multi-GPU Support with CUDA Backend + +The Polynomial API includes comprehensive support for multi-GPU environments, a crucial feature for leveraging the full computational power of systems equipped with multiple NVIDIA GPUs. This capability is part of the API's CUDA backend, which is designed to efficiently manage polynomial computations across different GPUs. + +### Setting the CUDA Device + +Like other components of the icicle framework, the Polynomial API allows explicit setting of the current CUDA device: + +```cpp +cudaSetDevice(int deviceID); +``` + +This function sets the active CUDA device. All subsequent operations that allocate or deal with polynomial data will be performed on this device. + +### Allocation Consistency + +Polynomials are always allocated on the current CUDA device at the time of their creation. It is crucial to ensure that the device context is correctly set before initiating any operation that involves memory allocation: + +```cpp +// Set the device before creating polynomials +cudaSetDevice(0); +Polynomial p1 = Polynomial::from_coefficients(coeffs, size); + +cudaSetDevice(1); +Polynomial p2 = Polynomial::from_coefficients(coeffs, size); +``` + +### Matching Devices for Operations + +When performing operations that result in the creation of new polynomials (such as addition or multiplication), it is imperative that both operands are on the same CUDA device. If the operands reside on different devices, an exception is thrown: + +```cpp +// Ensure both operands are on the same device +cudaSetDevice(0); +auto p3 = p1 + p2; // Throws an exception if p1 and p2 are not on the same device +``` + +### Device-Agnostic Operations + +Operations that do not involve the creation of new polynomials, such as computing the degree of a polynomial or performing in-place modifications, can be executed regardless of the current device setting: + +```cpp +// 'degree' and in-place operations do not require device matching +int deg = p1.degree(); +p1 += p2; // Valid if p1 and p2 are on the same device, throws otherwise +``` + +### Error Handling + +The API is designed to throw exceptions if operations are attempted across polynomials that are not located on the same GPU. This ensures that all polynomial operations are performed consistently and without data integrity issues due to device mismatches. + +### Best Practices + +To maximize the performance and avoid runtime errors in a multi-GPU setup, always ensure that: + +- The CUDA device is set correctly before polynomial allocation. +- Operations involving new polynomial creation are performed with operands on the same device. + +By adhering to these guidelines, developers can effectively harness the power of multiple GPUs to handle large-scale polynomial computations efficiently. diff --git a/docs/versioned_docs/version-2.8.0/icicle/primitives/image-1.png b/docs/versioned_docs/version-2.8.0/icicle/primitives/image-1.png new file mode 100644 index 000000000..cc0ba5916 Binary files /dev/null and b/docs/versioned_docs/version-2.8.0/icicle/primitives/image-1.png differ diff --git a/docs/versioned_docs/version-2.8.0/icicle/primitives/image-2.png b/docs/versioned_docs/version-2.8.0/icicle/primitives/image-2.png new file mode 100644 index 000000000..e59a16925 Binary files /dev/null and b/docs/versioned_docs/version-2.8.0/icicle/primitives/image-2.png differ diff --git a/docs/versioned_docs/version-2.8.0/icicle/primitives/image-3.png b/docs/versioned_docs/version-2.8.0/icicle/primitives/image-3.png new file mode 100644 index 000000000..75d3b4329 Binary files /dev/null and b/docs/versioned_docs/version-2.8.0/icicle/primitives/image-3.png differ diff --git a/docs/versioned_docs/version-2.8.0/icicle/primitives/image.png b/docs/versioned_docs/version-2.8.0/icicle/primitives/image.png new file mode 100644 index 000000000..0fa4826e9 Binary files /dev/null and b/docs/versioned_docs/version-2.8.0/icicle/primitives/image.png differ diff --git a/docs/versioned_docs/version-2.8.0/icicle/primitives/keccak.md b/docs/versioned_docs/version-2.8.0/icicle/primitives/keccak.md new file mode 100644 index 000000000..37ea9904d --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/primitives/keccak.md @@ -0,0 +1,75 @@ +# Keccak + +[Keccak](https://keccak.team/files/Keccak-implementation-3.2.pdf) is a cryptographic hash function designed by Guido Bertoni, Joan Daemen, Michaël Peeters, and Gilles Van Assche. It was selected as the winner of the NIST hash function competition, becoming the basis for the [SHA-3 standard](https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.202.pdf). + +Keccak operates on a message input of any length and produces a fixed-size hash output. The hash function is built upon the sponge construction, which involves absorbing the input data followed by squeezing out the hash value. + +At its core, Keccak consists of a permutation function operating on a state array. The permutation function employs a round function that operates iteratively on the state array. Each round consists of five main steps: + +- **Theta:** This step introduces diffusion by performing a bitwise XOR operation between the state and a linear combination of its neighboring columns. +- **Rho:** This step performs bit rotation operations on each lane of the state array. +- **Pi:** This step rearranges the positions of the lanes in the state array. +- **Chi:** This step applies a nonlinear mixing operation to each lane of the state array. +- **Iota:** This step introduces a round constant to the state array. + +## Using Keccak + +ICICLE Keccak supports batch hashing, which can be utilized for constructing a merkle tree or running multiple hashes in parallel. + +### Supported Bindings + +- [Golang](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/hash/keccak) +- [Rust](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/icicle-hash) + +### Example usage + +This is an example of running 1024 Keccak-256 hashes in parallel, where input strings are of size 136 bytes: + +```rust +use icicle_core::hash::HashConfig; +use icicle_cuda_runtime::memory::HostSlice; +use icicle_hash::keccak::keccak256; + +let config = HashConfig::default(); +let input_block_len = 136; +let number_of_hashes = 1024; + +let preimages = vec![1u8; number_of_hashes * input_block_len]; +let mut digests = vec![0u8; number_of_hashes * 64]; + +let preimages_slice = HostSlice::from_slice(&preimages); +let digests_slice = HostSlice::from_mut_slice(&mut digests); + +keccak256( + preimages_slice, + input_block_len as u32, + number_of_hashes as u32, + digests_slice, + &config, +) +.unwrap(); +``` + +### Merkle Tree + +You can build a keccak merkle tree using the corresponding functions: + +```rust +use icicle_core::tree::{merkle_tree_digests_len, TreeBuilderConfig}; +use icicle_cuda_runtime::memory::HostSlice; +use icicle_hash::keccak::build_keccak256_merkle_tree; + +let mut config = TreeBuilderConfig::default(); +config.arity = 2; +let height = 22; +let input_block_len = 136; +let leaves = vec![1u8; (1 << height) * input_block_len]; +let mut digests = vec![0u64; merkle_tree_digests_len((height + 1) as u32, 2, 1)]; + +let leaves_slice = HostSlice::from_slice(&leaves); +let digests_slice = HostSlice::from_mut_slice(&mut digests); + +build_keccak256_merkle_tree(leaves_slice, digests_slice, height, input_block_len, &config).unwrap(); +``` + +In the example above, a binary tree of height 22 is being built. Each leaf is considered to be a 136 byte long array. The leaves and digests are aligned in a flat array. You can also use keccak512 in `build_keccak512_merkle_tree` function. \ No newline at end of file diff --git a/docs/versioned_docs/version-2.8.0/icicle/primitives/msm.md b/docs/versioned_docs/version-2.8.0/icicle/primitives/msm.md new file mode 100644 index 000000000..7cb4a0abf --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/primitives/msm.md @@ -0,0 +1,195 @@ +# MSM - Multi scalar multiplication + +MSM stands for Multi scalar multiplication, its defined as: + + + M + S + M + ( + a + , + G + ) + = + + ∑ + + j + = + 0 + + + n + − + 1 + + + + a + j + + + G + j + + + +Where + +$G_j \in G$ - points from an Elliptic Curve group. + +$a_0, \ldots, a_n$ - Scalars + +$MSM(a, G) \in G$ - a single EC (elliptic curve) point + +In words, MSM is the sum of scalar and EC point multiplications. We can see from this definition that the core operations occurring are Modular Multiplication and Elliptic curve point addition. Its obvious that multiplication can be computed in parallel and then the products summed, making MSM inherently parallelizable. + +Accelerating MSM is crucial to a ZK protocol's performance due to the [large percent of run time](https://hackmd.io/@0xMonia/SkQ6-oRz3#Hardware-acceleration-in-action) they take when generating proofs. + +You can learn more about how MSMs work from this [video](https://www.youtube.com/watch?v=Bl5mQA7UL2I) and from our resource list on [Ingopedia](https://www.ingonyama.com/ingopedia/msm). + +## Supported Bindings + +- [Golang](../golang-bindings/msm.md) +- [Rust](../rust-bindings//msm.md) + +## Algorithm description + +We follow the bucket method algorithm. The GPU implementation consists of four phases: + +1. Preparation phase - The scalars are split into smaller scalars of `c` bits each. These are the bucket indices. The points are grouped according to their corresponding bucket index and the buckets are sorted by size. +2. Accumulation phase - Each bucket accumulates all of its points using a single thread. More than one thread is assigned to large buckets, in proportion to their size. A bucket is considered large if its size is above the large bucket threshold that is determined by the `large_bucket_factor` parameter. The large bucket threshold is the expected average bucket size times the `large_bucket_factor` parameter. +3. Buckets Reduction phase - bucket results are multiplied by their corresponding bucket number and each bucket module is reduced to a small number of final results. By default, this is done by an iterative algorithm which is highly parallel. Setting `is_big_triangle` to `true` will switch this phase to the running sum algorithm described in the above YouTube talk which is much less parallel. +4. Final accumulation phase - The final results from the last phase are accumulated using the double-and-add algorithm. + +## Batched MSM + +The MSM supports batch mode - running multiple MSMs in parallel. It's always better to use the batch mode instead of running single msms in serial as long as there is enough memory available. We support running a batch of MSMs that share the same points as well as a batch of MSMs that use different points. + +## MSM configuration + +```cpp + /** + * @struct MSMConfig + * Struct that encodes MSM parameters to be passed into the [MSM](@ref MSM) function. The intended use of this struct + * is to create it using [default_msm_config](@ref default_msm_config) function and then you'll hopefully only need to + * change a small number of default values for each of your MSMs. + */ + struct MSMConfig { + device_context::DeviceContext ctx; /**< Details related to the device such as its id and stream id. */ + int points_size; /**< Number of points in the MSM. If a batch of MSMs needs to be computed, this should be + * a number of different points. So, if each MSM re-uses the same set of points, this + * variable is set equal to the MSM size. And if every MSM uses a distinct set of + * points, it should be set to the product of MSM size and [batch_size](@ref + * batch_size). Default value: 0 (meaning it's equal to the MSM size). */ + int precompute_factor; /**< The number of extra points to pre-compute for each point. See the + * [precompute_msm_points](@ref precompute_msm_points) function, `precompute_factor` passed + * there needs to be equal to the one used here. Larger values decrease the + * number of computations to make, on-line memory footprint, but increase the static + * memory footprint. Default value: 1 (i.e. don't pre-compute). */ + int c; /**< \f$ c \f$ value, or "window bitsize" which is the main parameter of the "bucket + * method" that we use to solve the MSM problem. As a rule of thumb, larger value + * means more on-line memory footprint but also more parallelism and less computational + * complexity (up to a certain point). Currently pre-computation is independent of + * \f$ c \f$, however in the future value of \f$ c \f$ here and the one passed into the + * [precompute_msm_points](@ref precompute_msm_points) function will need to be identical. + * Default value: 0 (the optimal value of \f$ c \f$ is chosen automatically). */ + int bitsize; /**< Number of bits of the largest scalar. Typically equals the bitsize of scalar field, + * but if a different (better) upper bound is known, it should be reflected in this + * variable. Default value: 0 (set to the bitsize of scalar field). */ + int large_bucket_factor; /**< Variable that controls how sensitive the algorithm is to the buckets that occur + * very frequently. Useful for efficient treatment of non-uniform distributions of + * scalars and "top windows" with few bits. Can be set to 0 to disable separate + * treatment of large buckets altogether. Default value: 10. */ + int batch_size; /**< The number of MSMs to compute. Default value: 1. */ + bool are_scalars_on_device; /**< True if scalars are on device and false if they're on host. Default value: + * false. */ + bool are_scalars_montgomery_form; /**< True if scalars are in Montgomery form and false otherwise. Default value: + * true. */ + bool are_points_on_device; /**< True if points are on device and false if they're on host. Default value: false. */ + bool are_points_montgomery_form; /**< True if coordinates of points are in Montgomery form and false otherwise. + * Default value: true. */ + bool are_results_on_device; /**< True if the results should be on device and false if they should be on host. If set + * to false, `is_async` won't take effect because a synchronization is needed to + * transfer results to the host. Default value: false. */ + bool is_big_triangle; /**< Whether to do "bucket accumulation" serially. Decreases computational complexity + * but also greatly decreases parallelism, so only suitable for large batches of MSMs. + * Default value: false. */ + bool is_async; /**< Whether to run the MSM asynchronously. If set to true, the MSM function will be + * non-blocking and you'd need to synchronize it explicitly by running + * `cudaStreamSynchronize` or `cudaDeviceSynchronize`. If set to false, the MSM + * function will block the current CPU thread. */ + }; +``` + +## Choosing optimal parameters + +`is_big_triangle` should be `false` in almost all cases. It might provide better results only for very small MSMs (smaller than 2^8^) with a large batch (larger than 100) but this should be tested per scenario. +Large buckets exist in two cases: +1. When the scalar distribution isn't uniform. +2. When `c` does not divide the scalar bit-size. + +`large_bucket_factor` that is equal to 10 yields good results for most cases, but it's best to fine tune this parameter per `c` and per scalar distribution. +The two most important parameters for performance are `c` and the `precompute_factor`. They affect the number of EC additions as well as the memory size. When the points are not known in advance we cannot use precomputation. In this case the best `c` value is usually around $log_2(msmSize) - 4$. However, in most protocols the points are known in advanced and precomputation can be used unless limited by memory. Usually it's best to use maximum precomputation (such that we end up with only a single bucket module) combined we a `c` value around $log_2(msmSize) - 1$. + +## Memory usage estimation + +The main memory requirements of the MSM are the following: + +- Scalars - `sizeof(scalar_t) * msm_size * batch_size` +- Scalar indices - `~6 * sizeof(unsigned) * nof_bucket_modules * msm_size * batch_size` +- Points - `sizeof(affine_t) * msm_size * precomp_factor * batch_size` +- Buckets - `sizeof(projective_t) * nof_bucket_modules * 2^c * batch_size` + +where `nof_bucket_modules = ceil(ceil(bitsize / c) / precompute_factor)` + +During the MSM computation first the memory for scalars and scalar indices is allocated, then the indices are freed and points and buckets are allocated. This is why a good estimation for the required memory is the following formula: + +$max(scalars + scalarIndices, scalars + points + buckets)$ + +This gives a good approximation within 10% of the actual required memory for most cases. + +## Example parameters + +Here is a useful table showing optimal parameters for different MSMs. They are optimal for BLS12-377 curve when running on NVIDIA GeForce RTX 3090 Ti. This is the configuration used: + +```cpp + msm::MSMConfig config = { + ctx, // DeviceContext + N, // points_size + precomp_factor, // precompute_factor + user_c, // c + 0, // bitsize + 10, // large_bucket_factor + batch_size, // batch_size + false, // are_scalars_on_device + false, // are_scalars_montgomery_form + true, // are_points_on_device + false, // are_points_montgomery_form + true, // are_results_on_device + false, // is_big_triangle + true // is_async + }; +``` + +Here are the parameters and the results for the different cases: + +| MSM size | Batch size | Precompute factor | c | Memory estimation (GB) | Actual memory (GB) | Single MSM time (ms) | +| --- | --- | --- | --- | --- | --- | --- | +| 10 | 1 | 1 | 9 | 0.00227 | 0.00277 | 9.2 | +| 10 | 1 | 23 | 11 | 0.00259 | 0.00272 | 1.76 | +| 10 | 1000 | 1 | 7 | 0.94 | 1.09 | 0.051 | +| 10 | 1000 | 23 | 11 | 2.59 | 2.74 | 0.025 | +| 15 | 1 | 1 | 11 | 0.011 | 0.019 | 9.9 | +| 15 | 1 | 16 | 16 | 0.061 | 0.065 | 2.4 | +| 15 | 100 | 1 | 11 | 1.91 | 1.92 | 0.84 | +| 15 | 100 | 19 | 14 | 6.32 | 6.61 | 0.56 | +| 18 | 1 | 1 | 14 | 0.128 | 0.128 | 14.4 | +| 18 | 1 | 15 | 17 | 0.40 | 0.42 | 5.9 | +| 22 | 1 | 1 | 17 | 1.64 | 1.65 | 68 | +| 22 | 1 | 13 | 21 | 5.67 | 5.94 | 54 | +| 24 | 1 | 1 | 18 | 6.58 | 6.61 | 232 | +| 24 | 1 | 7 | 21 | 12.4 | 13.4 | 199 | + +The optimal values can vary per GPU and per curve. It is best to try a few combinations until you get the best results for your specific case. diff --git a/docs/versioned_docs/version-2.8.0/icicle/primitives/ntt.md b/docs/versioned_docs/version-2.8.0/icicle/primitives/ntt.md new file mode 100644 index 000000000..9816b3dce --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/primitives/ntt.md @@ -0,0 +1,159 @@ +# NTT - Number Theoretic Transform + +The Number Theoretic Transform (NTT) is a variant of the Fourier Transform used over finite fields, particularly those of integers modulo a prime number. NTT operates in a discrete domain and is used primarily in applications requiring modular arithmetic, such as cryptography and polynomial multiplication. + +NTT is defined similarly to the Discrete Fourier Transform (DFT), but instead of using complex roots of unity, it uses roots of unity within a finite field. The definition hinges on the properties of the finite field, specifically the existence of a primitive root of unity of order $N$ (where $N$ is typically a power of 2), and the modulo operation is performed with respect to a specific prime number that supports these roots. + +Formally, given a sequence of integers $a_0, a_1, ..., a_{N-1}$, the NTT of this sequence is another sequence of integers $A_0, A_1, ..., A_{N-1}$, computed as follows: + +$$ +A_k = \sum_{n=0}^{N-1} a_n \cdot \omega^{nk} \mod p +$$ + +where: + +- $N$ is the size of the input sequence and is a power of 2, +- $p$ is a prime number such that $p = kN + 1$ for some integer $k$, ensuring that $p$ supports the existence of $N$th roots of unity, +- $\omega$ is a primitive $N$th root of unity modulo $p$, meaning $\omega^N \equiv 1 \mod p$ and no smaller positive power of $\omega$ is congruent to 1 modulo $p$, +- $k$ ranges from 0 to $N-1$, and it indexes the output sequence. + +NTT is particularly useful because it enables efficient polynomial multiplication under modulo arithmetic, crucial for algorithms in cryptographic protocols and other areas requiring fast modular arithmetic operations. + +There exists also INTT which is the inverse operation of NTT. INTT can take as input an output sequence of integers from an NTT and reconstruct the original sequence. + +## Using NTT + +### Supported Bindings + +- [Golang](../golang-bindings/ntt.md) +- [Rust](../rust-bindings/ntt.md) + +### Examples + +- [Rust API examples](https://github.com/ingonyama-zk/icicle/blob/d84ffd2679a4cb8f8d1ac2ad2897bc0b95f4eeeb/examples/rust/ntt/src/main.rs#L1) + +- [C++ API examples](https://github.com/ingonyama-zk/icicle/blob/d84ffd2679a4cb8f8d1ac2ad2897bc0b95f4eeeb/examples/c%2B%2B/ntt/example.cu#L1) + +### Ordering + +The `Ordering` enum defines how inputs and outputs are arranged for the NTT operation, offering flexibility in handling data according to different algorithmic needs or compatibility requirements. It primarily affects the sequencing of data points for the transform, which can influence both performance and the compatibility with certain algorithmic approaches. The available ordering options are: + +- **`kNN` (Natural-Natural):** Both inputs and outputs are in their natural order. This is the simplest form of ordering, where data is processed in the sequence it is given, without any rearrangement. + +- **`kNR` (Natural-Reversed):** Inputs are in natural order, while outputs are in bit-reversed order. This ordering is typically used in algorithms that benefit from having the output in a bit-reversed pattern. + +- **`kRN` (Reversed-Natural):** Inputs are in bit-reversed order, and outputs are in natural order. This is often used with the Cooley-Tukey FFT algorithm. + +- **`kRR` (Reversed-Reversed):** Both inputs and outputs are in bit-reversed order. + +- **`kNM` (Natural-Mixed):** Inputs are provided in their natural order, while outputs are arranged in a digit-reversed (mixed) order. This ordering is good for mixed radix NTT operations, where the mixed or digit-reversed ordering of outputs is a generalization of the bit-reversal pattern seen in simpler, radix-2 cases. + +- **`kMN` (Mixed-Natural):** Inputs are in a digit-reversed (mixed) order, while outputs are restored to their natural order. This ordering would primarily be used for mixed radix NTT + +Choosing an algorithm is heavily dependent on your use case. For example Cooley-Tukey will often use `kRN` and Gentleman-Sande often uses `kNR`. + +### Modes + +NTT also supports two different modes `Batch NTT` and `Single NTT` + +Deciding weather to use `batch NTT` vs `single NTT` is highly dependent on your application and use case. + +#### Single NTT + +Single NTT will launch a single NTT computation. + +Choose this mode when your application requires processing individual NTT operations in isolation. + +#### Batch NTT Mode + +Batch NTT allows you to run many NTTs with a single API call. Batch NTT mode can significantly reduce read/write times as well as computation overhead by executing multiple NTT operations in parallel. Batch mode may also offer better utilization of computational resources (memory and compute). + +## Supported algorithms + +Our NTT implementation supports two algorithms `radix-2` and `mixed-radix`. + +### Radix 2 + +At its core, the Radix-2 NTT algorithm divides the problem into smaller sub-problems, leveraging the properties of "divide and conquer" to reduce the overall computational complexity. The algorithm operates on sequences whose lengths are powers of two. + +1. **Input Preparation:** + The input is a sequence of integers $a_0, a_1, \ldots, a_{N-1}, \text{ where } N$ is a power of two. + +2. **Recursive Decomposition:** + The algorithm recursively divides the input sequence into smaller sequences. At each step, it separates the sequence into even-indexed and odd-indexed elements, forming two subsequences that are then processed independently. + +3. **Butterfly Operations:** + The core computational element of the Radix-2 NTT is the "butterfly" operation, which combines pairs of elements from the sequences obtained in the decomposition step. + + Each butterfly operation involves multiplication by a "twiddle factor," which is a root of unity in the finite field, and addition or subtraction of the results, all performed modulo the prime modulus. + + $$ + X_k = (A_k + B_k \cdot W^k) \mod p + $$ + + $X_k$ - The output of the butterfly operation for the $k$-th element + + $A_k$ - an element from the even-indexed subset + + $B_k$ - an element from the odd-indexed subset + + $p$ - prime modulus + + $k$ - The index of the current operation within the butterfly or the transform stage + + The twiddle factors are precomputed to save runtime and improve performance. + +4. **Bit-Reversal Permutation:** + A final step involves rearranging the output sequence into the correct order. Due to the halving process in the decomposition steps, the elements of the transformed sequence are initially in a bit-reversed order. A bit-reversal permutation is applied to obtain the final sequence in natural order. + +### Mixed Radix + +The Mixed Radix NTT algorithm extends the concepts of the Radix-2 algorithm by allowing the decomposition of the input sequence based on various factors of its length. Specifically ICICLEs implementation splits the input into blocks of sizes 16, 32, or 64 compared to radix2 which is always splitting such that we end with NTT of size 2. This approach offers enhanced flexibility and efficiency, especially for input sizes that are composite numbers, by leveraging the "divide and conquer" strategy across multiple radices. + +The NTT blocks in Mixed Radix are implemented more efficiently based on winograd NTT but also optimized memory and register usage is better compared to Radix-2. + +Mixed Radix can reduce the number of stages required to compute for large inputs. + +1. **Input Preparation:** + The input to the Mixed Radix NTT is a sequence of integers $a_0, a_1, \ldots, a_{N-1}$, where $N$ is not strictly required to be a power of two. Instead, $N$ can be any composite number, ideally factorized into primes or powers of primes. + +2. **Factorization and Decomposition:** + Unlike the Radix-2 algorithm, which strictly divides the computational problem into halves, the Mixed Radix NTT algorithm implements a flexible decomposition approach which isn't limited to prime factorization. + + For example, an NTT of size 256 can be decomposed into two stages of $16 \times \text{NTT}_{16}$, leveraging a composite factorization strategy rather than decomposing into eight stages of $\text{NTT}_{2}$. This exemplifies the use of composite factors (in this case, $256 = 16 \times 16$) to apply smaller NTT transforms, optimizing computational efficiency by adapting the decomposition strategy to the specific structure of $N$. + +3. **Butterfly Operations with Multiple Radices:** + The Mixed Radix algorithm utilizes butterfly operations for various radix sizes. Each sub-transform involves specific butterfly operations characterized by multiplication with twiddle factors appropriate for the radix in question. + + The generalized butterfly operation for a radix-$r$ element can be expressed as: + + $$ + X_{k,r} = \sum_{j=0}^{r-1} (A_{j,k} \cdot W^{jk}) \mod p + $$ + + where: + + $X_{k,r}$ - is the output of the $radix-r$ butterfly operation for the $k-th$ set of inputs + + $A_{j,k}$ - represents the $j-th$ input element for the $k-th$ operation + + $W$ - is the twiddle factor + + $p$ - is the prime modulus + +4. **Recombination and Reordering:** + After applying the appropriate butterfly operations across all decomposition levels, the Mixed Radix algorithm recombines the results into a single output sequence. Due to the varied sizes of the sub-transforms, a more complex reordering process may be required compared to Radix-2. This involves digit-reversal permutations to ensure that the final output sequence is correctly ordered. + +### Which algorithm should I choose ? + +Both work only on inputs of power of 2 (e.g., 256, 512, 1024). + +Radix 2 is faster for small NTTs. A small NTT would be around logN = 16 and batch size 1. Radix 2 won't necessarily perform better for smaller `logn` with larger batches. + +Mixed radix on the other hand works better for larger NTTs with larger input sizes. + +Performance really depends on logn size, batch size, ordering, inverse, coset, coeff-field and which GPU you are using. + +For this reason we implemented our [heuristic auto-selection](https://github.com/ingonyama-zk/icicle/blob/main/icicle/src/ntt/ntt.cu#L573) which should choose the most efficient algorithm in most cases. + +We still recommend you benchmark for your specific use case if you think a different configuration would yield better results. diff --git a/docs/versioned_docs/version-2.8.0/icicle/primitives/overview.md b/docs/versioned_docs/version-2.8.0/icicle/primitives/overview.md new file mode 100644 index 000000000..67956f3e2 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/primitives/overview.md @@ -0,0 +1,12 @@ +# ICICLE Primitives + +This section of the documentation is dedicated to the ICICLE primitives, we will cover the usage and internal details of our primitives such as hashing algorithms, MSM and NTT. + + +## Supported primitives + + +- [MSM](./msm.md) +- [NTT](./ntt.md) +- [Keccak Hash](./keccak.md) +- [Poseidon Hash](./poseidon.md) diff --git a/docs/versioned_docs/version-2.8.0/icicle/primitives/poseidon.md b/docs/versioned_docs/version-2.8.0/icicle/primitives/poseidon.md new file mode 100644 index 000000000..5626701b9 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/primitives/poseidon.md @@ -0,0 +1,216 @@ +# Poseidon + +[Poseidon](https://eprint.iacr.org/2019/458.pdf) is a popular hash in the ZK ecosystem primarily because its optimized to work over large prime fields, a common setting for ZK proofs, thereby minimizing the number of multiplicative operations required. + +Poseidon has also been specifically designed to be efficient when implemented within ZK circuits, Poseidon uses far less constraints compared to other hash functions like Keccak or SHA-256 in the context of ZK circuits. + +Poseidon has been used in many popular ZK protocols such as Filecoin and [Plonk](https://drive.google.com/file/d/1bZZvKMQHaZGA4L9eZhupQLyGINkkFG_b/view?usp=drive_open). + +Our implementation of Poseidon is implemented in accordance with the optimized [Filecoin version](https://spec.filecoin.io/algorithms/crypto/poseidon/). + +Lets understand how Poseidon works. + +## Initialization + +Poseidon starts with the initialization of its internal state, which is composed of the input elements and some pre-generated constants. An initial round constant is added to each element of the internal state. Adding the round constants ensures the state is properly mixed from the beginning. + +This is done to prevent collisions and to prevent certain cryptographic attacks by ensuring that the internal state is sufficiently mixed and unpredictable. + +![Poseidon initialization of internal state added with pre-generated round constants](https://github.com/ingonyama-zk/icicle/assets/122266060/52257f5d-6097-47c4-8f17-7b6449b9d162) + +## Applying full and partial rounds + +To generate a secure hash output, the algorithm goes through a series of "full rounds" and "partial rounds" as well as transformations between these sets of rounds in the following order: + +```First full rounds -> apply S-box and Round constants -> partial rounds -> Last full rounds -> Apply S-box``` + +### Full rounds + +![Full round iterations consisting of S box operations, adding round constants, and a Full MDS matrix multiplication](https://github.com/ingonyama-zk/icicle/assets/122266060/e4ce0e98-b90b-4261-b83e-3cd8cce069cb) + +**Uniform Application of S-box:** In full rounds, the S-box (a non-linear transformation) is applied uniformly to every element of the hash function's internal state. This ensures a high degree of mixing and diffusion, contributing to the hash function's security. The functions S-box involves raising each element of the state to a certain power denoted by `α` a member of the finite field defined by the prime `p`; `α` can be different depending on the implementation and user configuration. + +**Linear Transformation:** After applying the S-box, a linear transformation is performed on the state. This involves multiplying the state by a MDS (Maximum Distance Separable) Matrix. which further diffuses the transformations applied by the S-box across the entire state. + +**Addition of Round Constants:** Each element of the state is then modified by adding a unique round constant. These constants are different for each round and are precomputed as part of the hash function's initialization. The addition of round constants ensures that even minor changes to the input produce significant differences in the output. + +### Partial Rounds + +![Partial round iterations consisting of selective S box operation, adding a round constant and performing an MDS multiplication with a sparse matrix](https://github.com/ingonyama-zk/icicle/assets/122266060/e8c198b4-7aa4-4b4d-9ec4-604e39e07692) + +**Selective Application of S-Box:** Partial rounds apply the S-box transformation to only one element of the internal state per round, rather than to all elements. This selective application significantly reduces the computational complexity of the hash function without compromising its security. The choice of which element to apply the S-box to can follow a specific pattern or be fixed, depending on the design of the hash function. + +**Linear Transformation and Round Constants:** A linear transformation is performed and round constants are added. The linear transformation in partial rounds can be designed to be less computationally intensive (this is done by using a sparse matrix) than in full rounds, further optimizing the function's efficiency. + +The user of Poseidon can often choose how many partial or full rounds he wishes to apply; more full rounds will increase security but degrade performance. The choice and balance is highly dependent on the use case. + +## Using Poseidon + +ICICLE Poseidon is implemented for GPU and parallelization is performed for each element of the state rather than for each state. +What that means is we calculate multiple hash-sums over multiple pre-images in parallel, rather than going block by block over the input vector. + +So for Poseidon of arity 2 and input of size 1024 * 2, we would expect 1024 elements of output. Which means each block would be of size 2 and that would result in 1024 Poseidon hashes being performed. + +### Supported Bindings + +[`Go`](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/golang/curves/bn254/poseidon/poseidon.go) +[`Rust`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/icicle-core/src/poseidon) + +### Constants + +Poseidon is extremely customizable and using different constants will produce different hashes, security levels and performance results. + +We support pre-calculated and optimized constants for each of the [supported curves](../core#supported-curves-and-operations).The constants can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/poseidon/constants) and are labeled clearly per curve `_poseidon.h`. + +If you wish to generate your own constants you can use our python script which can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/poseidon/constants/generate_parameters.py). + +Prerequisites: + +- Install python 3 +- `pip install poseidon-hash` +- `pip install galois==0.3.7` +- `pip install numpy` + +You will then need to modify the following values before running the script. + +```python +# Modify these +arity = 11 # we support arity 2, 4, 8 and 11. +p = 0x73EDA753299D7D483339D80809A1D80553BDA402FFFE5BFEFFFFFFFF00000001 # bls12-381 +# p = 0x12ab655e9a2ca55660b44d1e5c37b00159aa76fed00000010a11800000000001 # bls12-377 +# p = 0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001 # bn254 +# p = 0x1ae3a4617c510eac63b05c06ca1493b1a22d9f300f5138f1ef3622fba094800170b5d44300000008508c00000000001 # bw6-761 +prime_bit_len = 255 +field_bytes = 32 + +... + +# primitive_element = None +primitive_element = 7 # bls12-381 +# primitive_element = 22 # bls12-377 +# primitive_element = 5 # bn254 +# primitive_element = 15 # bw6-761 +``` + +### Rust API + +This is the most basic way to use the Poseidon API. + +```rust +let test_size = 1 << 10; +let arity = 2u32; +let ctx = get_default_device_context(); +let poseidon = Poseidon::load(arity, &ctx).unwrap(); +let config = HashConfig::default(); + +let inputs = vec![F::one(); test_size * arity as usize]; +let outputs = vec![F::zero(); test_size]; +let mut input_slice = HostOrDeviceSlice::on_host(inputs); +let mut output_slice = HostOrDeviceSlice::on_host(outputs); + +poseidon.hash_many::( + &mut input_slice, + &mut output_slice, + test_size as u32, + arity as u32, + 1, // Output length + &config, +) +.unwrap(); +``` + +The `HashConfig` can be modified, by default the inputs and outputs are set to be on `Host` for example. + +```rust +impl<'a> Default for HashConfig<'a> { + fn default() -> Self { + let ctx = get_default_device_context(); + Self { + ctx, + are_inputs_on_device: false, + are_outputs_on_device: false, + is_async: false, + } + } +} +``` + +In the example above `Poseidon::load(arity, &ctx).unwrap();` is used which will load the correct constants based on arity and curve. Its possible to [generate](#constants) your own constants and load them. + +```rust +let ctx = get_default_device_context(); +let custom_poseidon = Poseidon::new( + arity, // The arity of poseidon hash. The width will be equal to arity + 1 + alpha, // The S-box power + full_rounds_half, + partial_rounds, + round_constants, + mds_matrix, + non_sparse_matrix, + sparse_matrices, + domain_tag, + ctx, +) +.unwrap(); +``` + +## The Tree Builder + +The tree builder allows you to build Merkle trees using Poseidon. + +You can define both the tree's `height` and its `arity`. The tree `height` determines the number of layers in the tree, including the root and the leaf layer. The `arity` determines how many children each internal node can have. + +```rust +use icicle_bn254::tree::Bn254TreeBuilder; +use icicle_bn254::poseidon::Poseidon; + +let mut config = TreeBuilderConfig::default(); +let arity = 2; +config.arity = arity as u32; +let input_block_len = arity; +let leaves = vec![F::one(); (1 << height) * arity]; +let mut digests = vec![F::zero(); merkle_tree_digests_len((height + 1) as u32, arity as u32, 1)]; + +let leaves_slice = HostSlice::from_slice(&leaves); +let digests_slice = HostSlice::from_mut_slice(&mut digests); + +let ctx = device_context::DeviceContext::default(); +let hash = Poseidon::load(2, &ctx).unwrap(); + +let mut config = TreeBuilderConfig::default(); +config.keep_rows = 5; +Bn254TreeBuilder::build_merkle_tree( + leaves_slice, + digests_slice, + height, + input_block_len, + &hash, + &hash, + &config, +) +.unwrap(); +``` + +Similar to Poseidon, you can also configure the Tree Builder `TreeBuilderConfig::default()` + +- `keep_rows`: The number of rows which will be written to output, 0 will write all rows. +- `are_inputs_on_device`: Have the inputs been loaded to device memory ? +- `is_async`: Should the TreeBuilder run asynchronously? `False` will block the current CPU thread. `True` will require you call `cudaStreamSynchronize` or `cudaDeviceSynchronize` to retrieve the result. + +### Benchmarks + +We ran the Poseidon tree builder on: + +**CPU**: 12th Gen Intel(R) Core(TM) i9-12900K/ + +**GPU**: RTX 3090 Ti + +**Tree height**: 30 (2^29 elements) + +The benchmarks include copying data from and to the device. + +| Rows to keep parameter | Run time, Icicle | Supranational PC2 +| ----------- | ----------- | ----------- +| 10 | 9.4 seconds | 13.6 seconds +| 20 | 9.5 seconds | 13.6 seconds +| 29 | 13.7 seconds | 13.6 seconds diff --git a/docs/versioned_docs/version-2.8.0/icicle/primitives/poseidon2.md b/docs/versioned_docs/version-2.8.0/icicle/primitives/poseidon2.md new file mode 100644 index 000000000..cbf20bb5e --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/primitives/poseidon2.md @@ -0,0 +1,88 @@ +# Poseidon2 + +[Poseidon2](https://eprint.iacr.org/2023/323) is a recently released optimized version of Poseidon1. The two versions differ in two crucial points. First, Poseidon is a sponge hash function, while Poseidon2 can be either a sponge or a compression function depending on the use case. Secondly, Poseidon2 is instantiated by new and more efficient linear layers with respect to Poseidon. These changes decrease the number of multiplications in the linear layer by up to 90% and the number of constraints in Plonk circuits by up to 70%. This makes Poseidon2 currently the fastest arithmetization-oriented hash function without lookups. + + +## Using Poseidon2 + +ICICLE Poseidon2 is implemented for GPU and parallelization is performed for each state. +We calculate multiple hash-sums over multiple pre-images in parallel, rather than going block by block over the input vector. + +For example, for Poseidon2 of width 16, input rate 8, output elements 8 and input of size 1024 * 8, we would expect 1024 * 8 elements of output. Which means each input block would be of size 8, resulting in 1024 Poseidon2 hashes being performed. + +### Supported Bindings + +[`Rust`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/icicle-core/src/poseidon2) + +### Constants + +Poseidon2 is also extremely customizable and using different constants will produce different hashes, security levels and performance results. + +We support pre-calculated constants for each of the [supported curves](../core#supported-curves-and-operations). The constants can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/poseidon2/constants) and are labeled clearly per curve `_poseidon2.h`. + +You can also use your own set of constants as shown [here](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-fields/icicle-babybear/src/poseidon2/mod.rs#L290) + +### Rust API + +This is the most basic way to use the Poseidon2 API. + +```rust +let test_size = 1 << 10; +let width = 16; +let rate = 8; +let ctx = get_default_device_context(); +let poseidon = Poseidon2::load(width, rate, MdsType::Default, DiffusionStrategy::Default, &ctx).unwrap(); +let config = HashConfig::default(); + +let inputs = vec![F::one(); test_size * rate as usize]; +let outputs = vec![F::zero(); test_size]; +let mut input_slice = HostOrDeviceSlice::on_host(inputs); +let mut output_slice = HostOrDeviceSlice::on_host(outputs); + +poseidon.hash_many::( + &mut input_slice, + &mut output_slice, + test_size as u32, + rate as u32, + 8, // Output length + &config, +) +.unwrap(); +``` + +In the example above `Poseidon2::load(width, rate, MdsType::Default, DiffusionStrategy::Default, &ctx).unwrap();` is used to load the correct constants based on width and curve. Here, the default MDS matrices and diffusion are used. If you want to get a Plonky3 compliant version, set them to `MdsType::Plonky` and `DiffusionStrategy::Montgomery` respectively. + +## The Tree Builder + +Similar to Poseidon1, you can use Poseidon2 in a tree builder. + +```rust +use icicle_bn254::tree::Bn254TreeBuilder; +use icicle_bn254::poseidon2::Poseidon2; + +let mut config = TreeBuilderConfig::default(); +let arity = 2; +config.arity = arity as u32; +let input_block_len = arity; +let leaves = vec![F::one(); (1 << height) * arity]; +let mut digests = vec![F::zero(); merkle_tree_digests_len((height + 1) as u32, arity as u32, 1)]; + +let leaves_slice = HostSlice::from_slice(&leaves); +let digests_slice = HostSlice::from_mut_slice(&mut digests); + +let ctx = device_context::DeviceContext::default(); +let hash = Poseidon2::load(arity, arity, MdsType::Default, DiffusionStrategy::Default, &ctx).unwrap(); + +let mut config = TreeBuilderConfig::default(); +config.keep_rows = 5; +Bn254TreeBuilder::build_merkle_tree( + leaves_slice, + digests_slice, + height, + input_block_len, + &hash, + &hash, + &config, +) +.unwrap(); +``` diff --git a/docs/versioned_docs/version-2.8.0/icicle/rust-bindings.md b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings.md new file mode 100644 index 000000000..f3e6142f6 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings.md @@ -0,0 +1,87 @@ +# Rust bindings + +Rust bindings allow you to use ICICLE as a rust library. + +`icicle-core` defines all interfaces, macros and common methods. + +`icicle-cuda-runtime` defines DeviceContext which can be used to manage a specific GPU as well as wrapping common CUDA methods. + +`icicle-curves` implements all interfaces and macros from icicle-core for each curve. For example icicle-bn254 implements curve bn254. Each curve has its own build script which will build the CUDA libraries for that curve as part of the rust-toolchain build. + +## Using ICICLE Rust bindings in your project + +Simply add the following to your `Cargo.toml`. + +```toml +# GPU Icicle integration +icicle-cuda-runtime = { git = "https://github.com/ingonyama-zk/icicle.git" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git" } +icicle-bn254 = { git = "https://github.com/ingonyama-zk/icicle.git" } +``` + +`icicle-bn254` being the curve you wish to use and `icicle-core` and `icicle-cuda-runtime` contain ICICLE utilities and CUDA wrappers. + +If you wish to point to a specific ICICLE branch add `branch = ""` or `tag = ""` to the ICICLE dependency. For a specific commit add `rev = ""`. + +When you build your project ICICLE will be built as part of the build command. + +## How do the rust bindings work? + +The rust bindings are just rust wrappers for ICICLE Core static libraries which can be compiled. We integrate the compilation of the static libraries into rusts toolchain to make usage seamless and easy. This is achieved by [extending rusts build command](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-curves/icicle-bn254/build.rs). + +```rust +use cmake::Config; +use std::env::var; + +fn main() { + println!("cargo:rerun-if-env-changed=CXXFLAGS"); + println!("cargo:rerun-if-changed=../../../../icicle"); + + let cargo_dir = var("CARGO_MANIFEST_DIR").unwrap(); + let profile = var("PROFILE").unwrap(); + + let out_dir = Config::new("../../../../icicle") + .define("BUILD_TESTS", "OFF") //TODO: feature + .define("CURVE", "bn254") + .define("CMAKE_BUILD_TYPE", "Release") + .build_target("icicle") + .build(); + + println!("cargo:rustc-link-search={}/build", out_dir.display()); + + println!("cargo:rustc-link-lib=ingo_bn254"); + println!("cargo:rustc-link-lib=stdc++"); + // println!("cargo:rustc-link-search=native=/usr/local/cuda/lib64"); + println!("cargo:rustc-link-lib=cudart"); +} +``` + +## Supported curves, fields and operations + +### Supported curves and operations + +| Operation\Curve | bn254 | bls12_377 | bls12_381 | bw6-761 | grumpkin | +| --- | :---: | :---: | :---: | :---: | :---: | +| MSM | ✅ | ✅ | ✅ | ✅ | ✅ | +| G2 | ✅ | ✅ | ✅ | ✅ | ❌ | +| NTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| VecOps | ✅ | ✅ | ✅ | ✅ | ✅ | +| Polynomials | ✅ | ✅ | ✅ | ✅ | ❌ | +| Poseidon | ✅ | ✅ | ✅ | ✅ | ✅ | +| Merkle Tree | ✅ | ✅ | ✅ | ✅ | ✅ | + +### Supported fields and operations + +| Operation\Field | babybear | stark252 | +| --- | :---: | :---: | +| VecOps | ✅ | ✅ | +| Polynomials | ✅ | ✅ | +| NTT | ✅ | ✅ | +| Extension Field | ✅ | ❌ | + +### Supported hashes + +| Hash | Sizes | +| --- | :---: | +| Keccak | 256, 512 | diff --git a/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/ecntt.md b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/ecntt.md new file mode 100644 index 000000000..c2f790aef --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/ecntt.md @@ -0,0 +1,31 @@ +# ECNTT + +## ECNTT Method + +The `ecntt` function computes the Elliptic Curve Number Theoretic Transform (EC-NTT) or its inverse on a batch of points of a curve. + +```rust +pub fn ecntt( + input: &(impl HostOrDeviceSlice> + ?Sized), + dir: NTTDir, + cfg: &NTTConfig, + output: &mut (impl HostOrDeviceSlice> + ?Sized), +) -> IcicleResult<()> +where + C::ScalarField: FieldImpl, + ::Config: ECNTT, +{ + // ... function implementation ... +} +``` + +## Parameters + +- **`input`**: The input data as a slice of `Projective`. This represents points on a specific elliptic curve `C`. +- **`dir`**: The direction of the NTT. It can be `NTTDir::kForward` for forward NTT or `NTTDir::kInverse` for inverse NTT. +- **`cfg`**: The NTT configuration object of type `NTTConfig`. This object specifies parameters for the NTT computation, such as the batch size and algorithm to use. +- **`output`**: The output buffer to write the results into. This should be a slice of `Projective` with the same size as the input. + +## Return Value + +- **`IcicleResult<()>`**: This function returns an `IcicleResult` which is a wrapper type that indicates success or failure of the NTT computation. On success, it contains `Ok(())`. diff --git a/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/keccak.md b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/keccak.md new file mode 100644 index 000000000..9c8b231c7 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/keccak.md @@ -0,0 +1,96 @@ +# Keccak + +## Keccak Example + +```rust +use icicle_cuda_runtime::memory::{DeviceVec, HostSlice}; +use icicle_hash::keccak::{keccak256, HashConfig}; +use rand::{self, Rng}; + +fn main() { + let mut rng = rand::thread_rng(); + let initial_data: Vec = (0..120).map(|_| rng.gen::()).collect(); + println!("initial data: {}", hex::encode(&initial_data)); + let input = HostSlice::::from_slice(initial_data.as_slice()); + let mut output = DeviceVec::::cuda_malloc(32).unwrap(); + + let mut config = HashConfig::default(); + keccak256(input, initial_data.len() as i32, 1, &mut output[..], &mut config).expect("Failed to execute keccak256 hashing"); + + let mut output_host = vec![0_u8; 32]; + output.copy_to_host(HostSlice::from_mut_slice(&mut output_host[..])).unwrap(); + + println!("keccak256 result: {}", hex::encode(&output_host)); +} +``` + +## Keccak Methods + +```rust +pub fn keccak256( + input: &(impl HostOrDeviceSlice + ?Sized), + input_block_size: i32, + number_of_blocks: i32, + output: &mut (impl HostOrDeviceSlice + ?Sized), + config: &mut HashConfig, +) -> IcicleResult<()> + +pub fn keccak512( + input: &(impl HostOrDeviceSlice + ?Sized), + input_block_size: i32, + number_of_blocks: i32, + output: &mut (impl HostOrDeviceSlice + ?Sized), + config: &mut HashConfig, +) -> IcicleResult<()> +``` + +### Parameters + +- **`input`**: A slice containing the input data for the Keccak256 hash function. It can reside in either host memory or device memory. +- **`input_block_size`**: An integer specifying the size of the input data for a single hash. +- **`number_of_blocks`**: An integer specifying the number of results in the hash batch. +- **`output`**: A slice where the resulting hash will be stored. This slice can be in host or device memory. +- **`config`**: A pointer to a `HashConfig` object, which contains various configuration options for the Keccak256 operation. + +### Return Value + +- **`IcicleResult`**: Returns a CUDA error code indicating the success or failure of the Keccak256/Keccak512 operation. + +## HashConfig + +The `HashConfig` structure holds configuration parameters for the Keccak256/Keccak512 operation, allowing customization of its behavior to optimize performance based on the specifics of the operation or the underlying hardware. + +```rust +pub struct HashConfig<'a> { + pub ctx: DeviceContext<'a>, + pub are_inputs_on_device: bool, + pub are_outputs_on_device: bool, + pub is_async: bool, +} +``` + +### Fields + +- **`ctx`**: Device context containing details like device id and stream. +- **`are_inputs_on_device`**: Indicates if input data is located on the device. +- **`are_outputs_on_device`**: Indicates if output hash is stored on the device. +- **`is_async`**: If true, runs the Keccak256/Keccak512 operation asynchronously. + +### Usage + +Example initialization with default settings: + +```rust +let default_config = HashConfig::default(); +``` + +Customizing the configuration: + +```rust +let custom_config = NTTConfig { + ctx: custom_device_context, + are_inputs_on_device: true, + are_outputs_on_device: true, + is_async: false, +}; +``` \ No newline at end of file diff --git a/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/msm-pre-computation.md b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/msm-pre-computation.md new file mode 100644 index 000000000..687704fb2 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/msm-pre-computation.md @@ -0,0 +1,45 @@ +# MSM Pre computation + +To understand the theory behind MSM pre computation technique refer to Niall Emmart's [talk](https://youtu.be/KAWlySN7Hm8?feature=shared&t=1734). + +## `precompute_points` + +Precomputes bases for the multi-scalar multiplication (MSM) by extending each base point with its multiples, facilitating more efficient MSM calculations. + +```rust +pub fn precompute_points>( + points: &(impl HostOrDeviceSlice> + ?Sized), + msm_size: i32, + cfg: &MSMConfig, + output_bases: &mut DeviceSlice>, +) -> IcicleResult<()> +``` + +### Parameters + +- **`points`**: The original set of affine points (\(P_1, P_2, ..., P_n\)) to be used in the MSM. For batch MSM operations, this should include all unique points concatenated together. +- **`msm_size`**: The size of a single msm in order to determine optimal parameters. +- **`cfg`**: The MSM configuration parameters. +- **`output_bases`**: The output buffer for the extended bases. Its size must be `points.len() * precompute_factor`. This buffer should be allocated on the device for GPU computations. + +#### Returns + +`Ok(())` if the operation is successful, or an `IcicleResult` error otherwise. + +#### Description + +This function extends each provided base point $(P)$ with its multiples $(2^lP, 2^{2l}P, ..., 2^{(precompute_factor - 1) \cdot l}P)$, where $(l)$ is a level of precomputation determined by the `precompute_factor`. The extended set of points facilitates faster MSM computations by allowing the MSM algorithm to leverage precomputed multiples of base points, reducing the number of point additions required during the computation. + +The precomputation process is crucial for optimizing MSM operations, especially when dealing with large sets of points and scalars. By precomputing and storing multiples of the base points, the MSM function can more efficiently compute the scalar-point multiplications. + +#### Example Usage + +```rust +let cfg = MSMConfig::default(); +let precompute_factor = 4; // Number of points to precompute +let mut extended_bases = HostOrDeviceSlice::cuda_malloc(expected_size).expect("Failed to allocate memory for extended bases"); + +// Precompute the bases using the specified factor +precompute_points(&points, msm_size, &cfg, &mut extended_bases) + .expect("Failed to precompute bases"); +``` diff --git a/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/msm.md b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/msm.md new file mode 100644 index 000000000..8cfccf053 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/msm.md @@ -0,0 +1,170 @@ +# MSM + +## Example + +```rust +use icicle_bn254::curve::{CurveCfg, G1Projective, ScalarCfg}; +use icicle_core::{curve::Curve, msm, traits::GenerateRandom}; +use icicle_cuda_runtime::{memory::HostOrDeviceSlice, stream::CudaStream}; + +fn main() { + let size: usize = 1 << 10; // Define the number of points and scalars + + // Generate random points and scalars + println!("Generating random G1 points and scalars for BN254..."); + let points = CurveCfg::generate_random_affine_points(size); + let scalars = ScalarCfg::generate_random(size); + + // Wrap points and scalars in HostOrDeviceSlice for MSM + let points_host = HostOrDeviceSlice::Host(points); + let scalars_host = HostOrDeviceSlice::Host(scalars); + + // Allocate memory on the CUDA device for MSM results + let mut msm_results: HostOrDeviceSlice<'_, G1Projective> = HostOrDeviceSlice::cuda_malloc(1).expect("Failed to allocate CUDA memory for MSM results"); + + // Create a CUDA stream for asynchronous execution + let stream = CudaStream::create().expect("Failed to create CUDA stream"); + let mut cfg = msm::MSMConfig::default(); + cfg.ctx.stream = &stream; + cfg.is_async = true; // Enable asynchronous execution + + // Execute MSM on the device + println!("Executing MSM on device..."); + msm::msm(&scalars_host, &points_host, &cfg, &mut msm_results).expect("Failed to execute MSM"); + + // Synchronize CUDA stream to ensure MSM execution is complete + stream.synchronize().expect("Failed to synchronize CUDA stream"); + + // Optionally, move results to host for further processing or printing + println!("MSM execution complete."); +} +``` + +## MSM API Overview + +```rust +pub fn msm( + scalars: &HostOrDeviceSlice, + points: &HostOrDeviceSlice>, + cfg: &MSMConfig, + results: &mut HostOrDeviceSlice>, +) -> IcicleResult<()> +``` + +### Parameters + +- **`scalars`**: A buffer containing the scalar values to be multiplied with corresponding points. +- **`points`**: A buffer containing the points to be multiplied by the scalars. +- **`cfg`**: MSM configuration specifying additional parameters for the operation. +- **`results`**: A buffer where the results of the MSM operations will be stored. + +### MSM Config + +```rust +pub struct MSMConfig<'a> { + pub ctx: DeviceContext<'a>, + points_size: i32, + pub precompute_factor: i32, + pub c: i32, + pub bitsize: i32, + pub large_bucket_factor: i32, + batch_size: i32, + are_scalars_on_device: bool, + pub are_scalars_montgomery_form: bool, + are_points_on_device: bool, + pub are_points_montgomery_form: bool, + are_results_on_device: bool, + pub is_big_triangle: bool, + pub is_async: bool, +} +``` + +- **`ctx: DeviceContext`**: Specifies the device context, device id and the CUDA stream for asynchronous execution. +- **`point_size: i32`**: +- **`precompute_factor: i32`**: Determines the number of extra points to pre-compute for each point, affecting memory footprint and performance. +- **`c: i32`**: The "window bitsize," a parameter controlling the computational complexity and memory footprint of the MSM operation. +- **`bitsize: i32`**: The number of bits of the largest scalar, typically equal to the bit size of the scalar field. +- **`large_bucket_factor: i32`**: Adjusts the algorithm's sensitivity to frequently occurring buckets, useful for non-uniform scalar distributions. +- **`batch_size: i32`**: The number of MSMs to compute in a single batch, for leveraging parallelism. +- **`are_scalars_montgomery_form`**: Set to `true` if scalars are in montgomery form. +- **`are_points_montgomery_form`**: Set to `true` if points are in montgomery form. +- **`are_scalars_on_device: bool`**, **`are_points_on_device: bool`**, **`are_results_on_device: bool`**: Indicate whether the corresponding buffers are on the device memory. +- **`is_big_triangle`**: If `true` MSM will run in Large triangle accumulation if `false` Bucket accumulation will be chosen. Default value: false. +- **`is_async: bool`**: Whether to perform the MSM operation asynchronously. + +### Usage + +The `msm` function is designed to compute the sum of multiple scalar-point multiplications efficiently. It supports both single MSM operations and batched operations for increased performance. The configuration allows for detailed control over the execution environment and performance characteristics of the MSM operation. + +When performing MSM operations, it's crucial to match the size of the `scalars` and `points` arrays correctly and ensure that the `results` buffer is appropriately sized to hold the output. The `MSMConfig` should be set up to reflect the specifics of the operation, including whether the operation should be asynchronous and any device-specific settings. + +## How do I toggle between the supported algorithms? + +When creating your MSM Config you may state which algorithm you wish to use. `is_big_triangle=true` will activate Large triangle reduction and `is_big_triangle=false` will activate iterative reduction. + +```rust +... + +let mut cfg_bls12377 = msm::get_default_msm_config::(); + +// is_big_triangle will determine which algorithm to use +cfg_bls12377.is_big_triangle = true; + +msm::msm(&scalars, &points, &cfg, &mut msm_results).unwrap(); +... +``` + +You may reference the rust code [here](https://github.com/ingonyama-zk/icicle/blob/77a7613aa21961030e4e12bf1c9a78a2dadb2518/wrappers/rust/icicle-core/src/msm/mod.rs#L54). + +## How do I toggle between MSM modes? + +Toggling between MSM modes occurs automatically based on the number of results you are expecting from the `msm::msm` function. If you are expecting an array of `msm_results`, ICICLE will automatically split `scalars` and `points` into equal parts and run them as multiple MSMs in parallel. + +```rust +... + +let mut msm_result: HostOrDeviceSlice<'_, G1Projective> = HostOrDeviceSlice::cuda_malloc(1).unwrap(); +msm::msm(&scalars, &points, &cfg, &mut msm_result).unwrap(); + +... +``` + +In the example above we allocate a single expected result which the MSM method will interpret as `batch_size=1` and run a single MSM. + +In the next example, we are expecting 10 results which sets `batch_size=10` and runs 10 MSMs in batch mode. + +```rust +... + +let mut msm_results: HostOrDeviceSlice<'_, G1Projective> = HostOrDeviceSlice::cuda_malloc(10).unwrap(); +msm::msm(&scalars, &points, &cfg, &mut msm_results).unwrap(); + +... +``` + +Here is a [reference](https://github.com/ingonyama-zk/icicle/blob/77a7613aa21961030e4e12bf1c9a78a2dadb2518/wrappers/rust/icicle-core/src/msm/mod.rs#L108) to the code which automatically sets the batch size. For more MSM examples have a look [here](https://github.com/ingonyama-zk/icicle/blob/77a7613aa21961030e4e12bf1c9a78a2dadb2518/examples/rust/msm/src/main.rs#L1). + +## Parameters for optimal performance + +Please refer to the [primitive description](../primitives/msm#choosing-optimal-parameters) + +## Support for G2 group + +MSM also supports G2 group. + +Using MSM in G2 requires a G2 config, and of course your Points should also be G2 Points. + +```rust +... + +let scalars = HostOrDeviceSlice::Host(upper_scalars[..size].to_vec()); +let g2_points = HostOrDeviceSlice::Host(g2_upper_points[..size].to_vec()); +let mut g2_msm_results: HostOrDeviceSlice<'_, G2Projective> = HostOrDeviceSlice::cuda_malloc(1).unwrap(); +let mut g2_cfg = msm::get_default_msm_config::(); + +msm::msm(&scalars, &g2_points, &g2_cfg, &mut g2_msm_results).unwrap(); + +... +``` + +Here you can [find an example](https://github.com/ingonyama-zk/icicle/blob/5a96f9937d0a7176d88c766bd3ef2062b0c26c37/examples/rust/msm/src/main.rs#L114) of MSM on G2 Points. diff --git a/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/multi-gpu.md b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/multi-gpu.md new file mode 100644 index 000000000..8e36a5396 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/multi-gpu.md @@ -0,0 +1,202 @@ +# Multi GPU APIs + +To learn more about the theory of Multi GPU programming refer to [this part](../multi-gpu.md) of documentation. + +Here we will cover the core multi GPU apis and a [example](#a-multi-gpu-example) + + +## A Multi GPU example + +In this example we will display how you can + +1. Fetch the number of devices installed on a machine +2. For every GPU launch a thread and set an active device per thread. +3. Execute a MSM on each GPU + + + +```rust + +... + +let device_count = get_device_count().unwrap(); + +(0..device_count) + .into_par_iter() + .for_each(move |device_id| { + set_device(device_id).unwrap(); + + // you can allocate points and scalars_d here + + let mut cfg = MSMConfig::default_for_device(device_id); + cfg.ctx.stream = &stream; + cfg.is_async = true; + cfg.are_scalars_montgomery_form = true; + msm(&scalars_d, &HostOrDeviceSlice::on_host(points), &cfg, &mut msm_results).unwrap(); + + // collect and process results + }) + +... +``` + + +We use `get_device_count` to fetch the number of connected devices, device IDs will be `0, 1, 2, ..., device_count - 1` + +[`into_par_iter`](https://docs.rs/rayon/latest/rayon/iter/trait.IntoParallelIterator.html#tymethod.into_par_iter) is a parallel iterator, you should expect it to launch a thread for every iteration. + +We then call `set_device(device_id).unwrap();` it should set the context of that thread to the selected `device_id`. + +Any data you now allocate from the context of this thread will be linked to the `device_id`. We create our `MSMConfig` with the selected device ID `let mut cfg = MSMConfig::default_for_device(device_id);`, behind the scene this will create for us a `DeviceContext` configured for that specific GPU. + +We finally call our `msm` method. + + +## Device management API + +To streamline device management we offer as part of `icicle-cuda-runtime` package methods for dealing with devices. + +#### [`set_device`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L6) + +Sets the current CUDA device by its ID, when calling `set_device` it will set the current thread to a CUDA device. + +**Parameters:** + +- **`device_id: usize`**: The ID of the device to set as the current device. Device IDs start from 0. + +**Returns:** + +- **`CudaResult<()>`**: An empty result indicating success if the device is set successfully. In case of failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if the specified device ID is invalid or if a CUDA-related error occurs during the operation. + +**Example:** + +```rust +let device_id = 0; // Device ID to set +match set_device(device_id) { + Ok(()) => println!("Device set successfully."), + Err(e) => eprintln!("Failed to set device: {:?}", e), +} +``` + +#### [`get_device_count`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L10) + +Retrieves the number of CUDA devices available on the machine. + +**Returns:** + +- **`CudaResult`**: The number of available CUDA devices. On success, contains the count of CUDA devices. On failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if a CUDA-related error occurs during the retrieval of the device count. + +**Example:** + +```rust +match get_device_count() { + Ok(count) => println!("Number of devices available: {}", count), + Err(e) => eprintln!("Failed to get device count: {:?}", e), +} +``` + +#### [`get_device`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L15) + +Retrieves the ID of the current CUDA device. + +**Returns:** + +- **`CudaResult`**: The ID of the current CUDA device. On success, contains the device ID. On failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if a CUDA-related error occurs during the retrieval of the current device ID. + +**Example:** + +```rust +match get_device() { + Ok(device_id) => println!("Current device ID: {}", device_id), + Err(e) => eprintln!("Failed to get current device: {:?}", e), +} +``` + +## Device context API + +The `DeviceContext` is embedded into `NTTConfig`, `MSMConfig` and `PoseidonConfig`, meaning you can simply pass a `device_id` to your existing config and the same computation will be triggered on a different device. + +#### [`DeviceContext`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L11) + +Represents the configuration a CUDA device, encapsulating the device's stream, ID, and memory pool. The default device is always `0`. + +```rust +pub struct DeviceContext<'a> { + pub stream: &'a CudaStream, + pub device_id: usize, + pub mempool: CudaMemPool, +} +``` + +##### Fields + +- **`stream: &'a CudaStream`** + + A reference to a `CudaStream`. This stream is used for executing CUDA operations. By default, it points to a null stream CUDA's default execution stream. + +- **`device_id: usize`** + + The index of the GPU currently in use. The default value is `0`, indicating the first GPU in the system. + + In some cases assuming `CUDA_VISIBLE_DEVICES` was configured, for example as `CUDA_VISIBLE_DEVICES=2,3,7` in the system with 8 GPUs - the `device_id=0` will correspond to GPU with id 2. So the mapping may not always be a direct reflection of the number of GPUs installed on a system. + +- **`mempool: CudaMemPool`** + + Represents the memory pool used for CUDA memory allocations. The default is set to a null pointer, which signifies the use of the default CUDA memory pool. + +##### Implementation Notes + +- The `DeviceContext` structure is cloneable and can be debugged, facilitating easier logging and duplication of contexts when needed. + + +#### [`DeviceContext::default_for_device(device_id: usize) -> DeviceContext<'static>`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L30) + +Provides a default `DeviceContext` with system-wide defaults, ideal for straightforward setups. + +#### Returns + +A `DeviceContext` instance configured with: +- The default stream (`null_mut()`). +- The default device ID (`0`). +- The default memory pool (`null_mut()`). + +#### Parameters + +- **`device_id: usize`**: The ID of the device for which to create the context. + +#### Returns + +A `DeviceContext` instance with the provided `device_id` and default settings for the stream and memory pool. + + +#### [`check_device(device_id: i32)`](https://github.com/vhnatyk/icicle/blob/eef6876b037a6b0797464e7cdcf9c1ecfcf41808/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L42) + +Validates that the specified `device_id` matches the ID of the currently active device, ensuring operations are targeted correctly. + +#### Parameters + +- **`device_id: i32`**: The device ID to verify against the currently active device. + +#### Behavior + +- **`Panics`** if the `device_id` does not match the active device's ID, preventing cross-device operation errors. + +#### Example + +```rust +let device_id: i32 = 0; // Example device ID +check_device(device_id); +// Ensures that the current context is correctly set for the specified device ID. +``` diff --git a/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/ntt.md b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/ntt.md new file mode 100644 index 000000000..9d0b8f62f --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/ntt.md @@ -0,0 +1,200 @@ +# NTT + +## Example + +```rust +use icicle_bn254::curve::{ScalarCfg, ScalarField}; +use icicle_core::{ntt::{self, NTT}, traits::GenerateRandom}; +use icicle_cuda_runtime::{device_context::DeviceContext, memory::HostOrDeviceSlice, stream::CudaStream}; + +fn main() { + let size = 1 << 12; // Define the size of your input, e.g., 2^10 + + let icicle_omega = ::get_root_of_unity( + size.try_into() + .unwrap(), + ) + + // Generate random inputs + println!("Generating random inputs..."); + let scalars = HostOrDeviceSlice::Host(ScalarCfg::generate_random(size)); + + // Allocate memory on CUDA device for NTT results + let mut ntt_results: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::cuda_malloc(size).expect("Failed to allocate CUDA memory"); + + // Create a CUDA stream + let stream = CudaStream::create().expect("Failed to create CUDA stream"); + let ctx = DeviceContext::default(); // Assuming default device context + ScalarCfg::initialize_domain(ScalarField::from_ark(icicle_omega), &ctx, true).unwrap(); + + // Configure NTT + let mut cfg = ntt::NTTConfig::default(); + cfg.ctx.stream = &stream; + cfg.is_async = true; // Set to true for asynchronous execution + + // Execute NTT on device + println!("Executing NTT on device..."); + ntt::ntt(&scalars, ntt::NTTDir::kForward, &cfg, &mut ntt_results).expect("Failed to execute NTT"); + + // Synchronize CUDA stream to ensure completion + stream.synchronize().expect("Failed to synchronize CUDA stream"); + + // Optionally, move results to host for further processing or verification + println!("NTT execution complete."); +} +``` + +## NTT API overview + +```rust +pub fn ntt( + input: &HostOrDeviceSlice, + dir: NTTDir, + cfg: &NTTConfig, + output: &mut HostOrDeviceSlice, +) -> IcicleResult<()> +``` + +`ntt:ntt` expects: + +- **`input`** - buffer to read the inputs of the NTT from. +- **`dir`** - whether to compute forward or inverse NTT. +- **`cfg`** - config used to specify extra arguments of the NTT. +- **`output`** - buffer to write the NTT outputs into. Must be of the same size as input. + +The `input` and `output` buffers can be on device or on host. Being on host means that they will be transferred to device during runtime. + +### NTT Config + +```rust +pub struct NTTConfig<'a, S> { + pub ctx: DeviceContext<'a>, + pub coset_gen: S, + pub batch_size: i32, + pub columns_batch: bool, + pub ordering: Ordering, + are_inputs_on_device: bool, + are_outputs_on_device: bool, + pub is_async: bool, + pub ntt_algorithm: NttAlgorithm, +} +``` + +The `NTTConfig` struct is a configuration object used to specify parameters for an NTT instance. + +#### Fields + +- **`ctx: DeviceContext<'a>`**: Specifies the device context, including the device ID and the stream ID. + +- **`coset_gen: S`**: Defines the coset generator used for coset (i)NTTs. By default, this is set to `S::one()`, indicating that no coset is being used. + +- **`batch_size: i32`**: Determines the number of NTTs to compute in a single batch. The default value is 1, meaning that operations are performed on individual inputs without batching. Batch processing can significantly improve performance by leveraging parallelism in GPU computations. + +- **`columns_batch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. Defaults to `false`. + +- **`ordering: Ordering`**: Controls the ordering of inputs and outputs for the NTT operation. This field can be used to specify decimation strategies (in time or in frequency) and the type of butterfly algorithm (Cooley-Tukey or Gentleman-Sande). The ordering is crucial for compatibility with various algorithmic approaches and can impact the efficiency of the NTT. + +- **`are_inputs_on_device: bool`**: Indicates whether the input data has been preloaded on the device memory. If `false` inputs will be copied from host to device. + +- **`are_outputs_on_device: bool`**: Indicates whether the output data is preloaded in device memory. If `false` outputs will be copied from host to device. If the inputs and outputs are the same pointer NTT will be computed in place. + +- **`is_async: bool`**: Specifies whether the NTT operation should be performed asynchronously. When set to `true`, the NTT function will not block the CPU, allowing other operations to proceed concurrently. Asynchronous execution requires careful synchronization to ensure data integrity and correctness. + +- **`ntt_algorithm: NttAlgorithm`**: Can be one of `Auto`, `Radix2`, `MixedRadix`. +`Auto` will select `Radix 2` or `Mixed Radix` algorithm based on heuristics. +`Radix2` and `MixedRadix` will force the use of an algorithm regardless of the input size or other considerations. You should use one of these options when you know for sure that you want to + +#### Usage + +Example initialization with default settings: + +```rust +let default_config = NTTConfig::default(); +``` + +Customizing the configuration: + +```rust +let custom_config = NTTConfig { + ctx: custom_device_context, + coset_gen: my_coset_generator, + batch_size: 10, + columns_batch: false, + ordering: Ordering::kRN, + are_inputs_on_device: true, + are_outputs_on_device: true, + is_async: false, + ntt_algorithm: NttAlgorithm::MixedRadix, +}; +``` + +### Modes + +NTT supports two different modes `Batch NTT` and `Single NTT` + +You may toggle between single and batch NTT by simply configure `batch_size` to be larger then 1 in your `NTTConfig`. + +```rust +let mut cfg = ntt::get_default_ntt_config::(); +cfg.batch_size = 10 // your ntt using this config will run in batch mode. +``` + +`batch_size=1` would keep our NTT in single NTT mode. + +Deciding weather to use `batch NTT` vs `single NTT` is highly dependent on your application and use case. + +### Initializing the NTT Domain + +Before performing NTT operations, its necessary to initialize the NTT domain, It only needs to be called once per GPU since the twiddles are cached. + +```rust +ScalarCfg::initialize_domain(ScalarField::from_ark(icicle_omega), &ctx, true).unwrap(); +``` + +### `initialize_domain` + +```rust +pub fn initialize_domain(primitive_root: F, ctx: &DeviceContext, fast_twiddles: bool) -> IcicleResult<()> +where + F: FieldImpl, + ::Config: NTT; +``` + +#### Parameters + +- **`primitive_root`**: The primitive root of unity, chosen based on the maximum NTT size required for the computations. It must be of an order that is a power of two. This root is used to generate twiddle factors that are essential for the NTT operations. + +- **`ctx`**: A reference to a `DeviceContext` specifying which device and stream the computation should be executed on. + +#### Returns + +- **`IcicleResult<()>`**: Will return an error if the operation fails. + +#### Parameters + +- **`primitive_root`**: The primitive root of unity, chosen based on the maximum NTT size required for the computations. It must be of an order that is a power of two. This root is used to generate twiddle factors that are essential for the NTT operations. + +- **`ctx`**: A reference to a `DeviceContext` specifying which device and stream the computation should be executed on. + +#### Returns + +- **`IcicleResult<()>`**: Will return an error if the operation fails. + +### Releasing the domain + +The `release_domain` function is responsible for releasing the resources associated with a specific domain in the CUDA device context. + +```rust +pub fn release_domain(ctx: &DeviceContext) -> IcicleResult<()> +where + F: FieldImpl, + ::Config: NTT +``` + +#### Parameters + +- **`ctx`**: A reference to a `DeviceContext` specifying which device and stream the computation should be executed on. + +#### Returns + +The function returns an `IcicleResult<()>`, which represents the result of the operation. If the operation is successful, the function returns `Ok(())`, otherwise it returns an error. diff --git a/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/polynomials.md b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/polynomials.md new file mode 100644 index 000000000..c168aff48 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/polynomials.md @@ -0,0 +1,287 @@ +# Rust FFI Bindings for Univariate Polynomial + +:::note +Please refer to the Polynomials overview page for a deep overview. This section is a brief description of the Rust FFI bindings. +::: + +This documentation is designed to provide developers with a clear understanding of how to utilize the Rust bindings for polynomial operations efficiently and effectively, leveraging the robust capabilities of both Rust and C++ in their applications. + +## Introduction + +The Rust FFI bindings for the Univariate Polynomial serve as a "shallow wrapper" around the underlying C++ implementation. These bindings provide a straightforward Rust interface that directly calls functions from a C++ library, effectively bridging Rust and C++ operations. The Rust layer handles simple interface translations without delving into complex logic or data structures, which are managed on the C++ side. This design ensures efficient data handling, memory management, and execution of polynomial operations directly via C++. +Currently, these bindings are tailored specifically for polynomials where the coefficients, domain, and images are represented as scalar fields. + +## Initialization Requirements + +Before utilizing any functions from the polynomial API, it is mandatory to initialize the appropriate polynomial backend (e.g., CUDA). Additionally, the NTT (Number Theoretic Transform) domain must also be initialized, as the CUDA backend relies on this for certain operations. Failing to properly initialize these components can result in errors. + +:::note +**Field-Specific Initialization Requirement** + +The ICICLE library is structured such that each field or curve has its dedicated library implementation. As a result, initialization must be performed individually for each field or curve to ensure the correct setup and functionality of the library. +::: + +## Core Trait: `UnivariatePolynomial` + +The `UnivariatePolynomial` trait encapsulates the essential functionalities required for managing univariate polynomials in the Rust ecosystem. This trait standardizes the operations that can be performed on polynomials, regardless of the underlying implementation details. It allows for a unified approach to polynomial manipulation, providing a suite of methods that are fundamental to polynomial arithmetic. + +### Trait Definition + +```rust +pub trait UnivariatePolynomial +where + Self::Field: FieldImpl, + Self::FieldConfig: FieldConfig, +{ + type Field: FieldImpl; + type FieldConfig: FieldConfig; + + // Methods to create polynomials from coefficients or roots-of-unity evaluations. + fn from_coeffs + ?Sized>(coeffs: &S, size: usize) -> Self; + fn from_rou_evals + ?Sized>(evals: &S, size: usize) -> Self; + + // Method to divide this polynomial by another, returning quotient and remainder. + fn divide(&self, denominator: &Self) -> (Self, Self) where Self: Sized; + + // Method to divide this polynomial by the vanishing polynomial 'X^N-1'. + fn div_by_vanishing(&self, degree: u64) -> Self; + + // Methods to add or subtract a monomial in-place. + fn add_monomial_inplace(&mut self, monomial_coeff: &Self::Field, monomial: u64); + fn sub_monomial_inplace(&mut self, monomial_coeff: &Self::Field, monomial: u64); + + // Method to slice the polynomial, creating a sub-polynomial. + fn slice(&self, offset: u64, stride: u64, size: u64) -> Self; + + // Methods to return new polynomials containing only the even or odd terms. + fn even(&self) -> Self; + fn odd(&self) -> Self; + + // Method to evaluate the polynomial at a given domain point. + fn eval(&self, x: &Self::Field) -> Self::Field; + + // Method to evaluate the polynomial over a domain and store the results. + fn eval_on_domain + ?Sized, E: HostOrDeviceSlice + ?Sized>( + &self, + domain: &D, + evals: &mut E, + ); + + // Method to evaluate the polynomial over the roots-of-unity domain for power-of-two sized domain + fn eval_on_rou_domain + ?Sized>(&self, domain_log_size: u64, evals: &mut E); + + // Method to retrieve a coefficient at a specific index. + fn get_coeff(&self, idx: u64) -> Self::Field; + + // Method to copy coefficients into a provided slice. + fn copy_coeffs + ?Sized>(&self, start_idx: u64, coeffs: &mut S); + + // Method to get the degree of the polynomial. + fn degree(&self) -> i64; +} +``` + +## `DensePolynomial` Struct + +The DensePolynomial struct represents a dense univariate polynomial in Rust, leveraging a handle to manage its underlying memory within the CUDA device context. This struct acts as a high-level abstraction over complex C++ memory management practices, facilitating the integration of high-performance polynomial operations through Rust's Foreign Function Interface (FFI) bindings. + +```rust +pub struct DensePolynomial { + handle: PolynomialHandle, +} +``` + +### Traits implementation and methods + +#### `Drop` + +Ensures proper resource management by releasing the CUDA memory when a DensePolynomial instance goes out of scope. This prevents memory leaks and ensures that resources are cleaned up correctly, adhering to Rust's RAII (Resource Acquisition Is Initialization) principles. + +#### `Clone` + +Provides a way to create a new instance of a DensePolynomial with its own unique handle, thus duplicating the polynomial data in the CUDA context. Cloning is essential since the DensePolynomial manages external resources, which cannot be safely shared across instances without explicit duplication. + +#### Operator Overloading: `Add`, `Sub`, `Mul`, `Rem`, `Div` + +These traits are implemented for references to DensePolynomial (i.e., &DensePolynomial), enabling natural mathematical operations such as addition (+), subtraction (-), multiplication (*), division (/), and remainder (%). This syntactic convenience allows users to compose complex polynomial expressions in a way that is both readable and expressive. + +#### Key Methods + +In addition to the traits, the following methods are implemented: + +```rust +impl DensePolynomial { + pub fn init_cuda_backend() -> bool {...} + // Returns a mutable slice of the polynomial coefficients on the device + pub fn coeffs_mut_slice(&mut self) -> &mut DeviceSlice {...} +} +``` + +## Flexible Memory Handling With `HostOrDeviceSlice` + +The DensePolynomial API is designed to accommodate a wide range of computational environments by supporting both host and device memory through the `HostOrDeviceSlice` trait. This approach ensures that polynomial operations can be seamlessly executed regardless of where the data resides, making the API highly adaptable and efficient for various hardware configurations. + +### Overview of `HostOrDeviceSlice` + +The HostOrDeviceSlice is a Rust trait that abstracts over slices of memory that can either be on the host (CPU) or the device (GPU), as managed by CUDA. This abstraction is crucial for high-performance computing scenarios where data might need to be moved between different memory spaces depending on the operations being performed and the specific hardware capabilities available. + +### Usage in API Functions + +Functions within the DensePolynomial API that deal with polynomial coefficients or evaluations use the HostOrDeviceSlice trait to accept inputs. This design allows the functions to be agnostic of the actual memory location of the data, whether it's in standard system RAM accessible by the CPU or in GPU memory accessible by CUDA cores. + +```rust +// Assume `coeffs` could either be in host memory or CUDA device memory +let coeffs: DeviceSlice = DeviceVec::::cuda_malloc(coeffs_len).unwrap(); +let p_from_coeffs = PolynomialBabyBear::from_coeffs(&coeffs, coeffs.len()); + +// Similarly for evaluations from roots of unity +let evals: HostSlice = HostSlice::from_slice(&host_memory_evals); +let p_from_evals = PolynomialBabyBear::from_rou_evals(&evals, evals.len()); + +// Same applies for any API that accepts HostOrDeviceSlice +``` + +## Usage + +This section outlines practical examples demonstrating how to utilize the `DensePolynomial` Rust API. The API is flexible, supporting multiple scalar fields. Below are examples showing how to use polynomials defined over different fields and perform a variety of operations. + +### Initialization and Basic Operations + +First, choose the appropriate field implementation for your polynomial operations, initializing the CUDA backend if necessary + +```rust +use icicle_babybear::polynomials::DensePolynomial as PolynomialBabyBear; + +// Initialize the CUDA backend for polynomial operations +PolynomialBabyBear::init_cuda_backend(); +let f = PolynomialBabyBear::from_coeffs(...); + +// now use f by calling the implemented traits + +// For operations over another field, such as BN254 +use icicle_bn254::polynomials::DensePolynomial as PolynomialBn254; +// Use PolynomialBn254 similarly +``` + +### Creation + +Polynomials can be created from coefficients or evaluations: + +```rust +let coeffs = ...; +let p_from_coeffs = PolynomialBabyBear::from_coeffs(HostSlice::from_slice(&coeffs), size); + +let evals = ...; +let p_from_evals = PolynomialBabyBear::from_rou_evals(HostSlice::from_slice(&evals), size); + +``` + +### Arithmetic Operations + +Utilize overloaded operators for intuitive mathematical expressions: + +```rust +let add = &f + &g; // Addition +let sub = &f - &g; // Subtraction +let mul = &f * &g; // Multiplication +let mul_scalar = &f * &scalar; // Scalar multiplication +``` + +### Division and Remainder + +Compute quotient and remainder or perform division by a vanishing polynomial: + +```rust +let (q, r) = f.divide(&g); // Compute both quotient and remainder +let q = &f / &g; // Quotient +let r = &f % &g; // Remainder + +let h = f.div_by_vanishing(N); // Division by V(x) = X^N - 1 + +``` + +### Monomial Operations + +Add or subtract monomials in-place for efficient polynomial manipulation: + +```rust +f.add_monomial_inplace(&three, 1 /*monmoial*/); // Adds 3*x to f +f.sub_monomial_inplace(&one, 0 /*monmoial*/); // Subtracts 1 from f +``` + +### Slicing + +Extract specific components: + +```rust +let even = f.even(); // Polynomial of even-indexed terms +let odd = f.odd(); // Polynomial of odd-indexed terms +let arbitrary_slice = f.slice(offset, stride, size); +``` + +### Evaluate + +Evaluate the polynoomial: + +```rust +let x = rand(); // Random field element +let f_x = f.eval(&x); // Evaluate f at x + +// Evaluate on a predefined domain +let domain = [one, two, three]; +let mut host_evals = vec![ScalarField::zero(); domain.len()]; +f.eval_on_domain(HostSlice::from_slice(&domain), HostSlice::from_mut_slice(&mut host_evals)); + +// Evaluate on roots-of-unity-domain +let domain_log_size = 4; +let mut device_evals = DeviceVec::::cuda_malloc(1 << domain_log_size).unwrap(); +f.eval_on_rou_domain(domain_log_size, &mut device_evals[..]); +``` + +### Read coefficients + +Read or copy polynomial coefficients for further processing: + +```rust +let x_squared_coeff = f.get_coeff(2); // Coefficient of x^2 + +// Copy coefficients to a device-specific memory space +let mut device_mem = DeviceVec::::cuda_malloc(coeffs.len()).unwrap(); +f.copy_coeffs(0, &mut device_mem[..]); +``` + +### Polynomial Degree + +Determine the highest power of the variable with a non-zero coefficient: + +```rust +let deg = f.degree(); // Degree of the polynomial +``` + +### Memory Management: Views (rust slices) + +Rust enforces correct usage of views at compile time, eliminating the need for runtime checks: + +```rust +let mut f = Poly::from_coeffs(HostSlice::from_slice(&coeffs), size); + +// Obtain a mutable slice of coefficients as a DeviceSlice +let coeffs_slice_dev = f.coeffs_mut_slice(); + +// Operations on f are restricted here due to mutable borrow of coeffs_slice_dev + +// Compute evaluations or perform other operations directly using the slice +// example: evaluate f on a coset of roots-of-unity. Computing from GPU to HOST/GPU +let mut config: NTTConfig<'_, F> = NTTConfig::default(); +config.coset_gen = /*some coset gen*/; +let mut coset_evals = vec![F::zero(); coeffs_slice_dev.len()]; +ntt( + coeffs_slice_dev, + NTTDir::kForward, + &config, + HostSlice::from_mut_slice(&mut coset_evals), +) +.unwrap(); + +// now can f can be borrowed once again +``` diff --git a/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/vec-ops.md b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/vec-ops.md new file mode 100644 index 000000000..e8cafdfda --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/icicle/rust-bindings/vec-ops.md @@ -0,0 +1,207 @@ +# Vector Operations API + +Our vector operations API which is part of `icicle-cuda-runtime` package, includes fundamental methods for addition, subtraction, and multiplication of vectors, with support for both host and device memory. + +## Examples + +### Addition of Scalars + +```rust +use icicle_bn254::curve::{ScalarCfg, ScalarField}; +use icicle_core::vec_ops::{add_scalars}; + +let test_size = 1 << 18; + +let a: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); +let b: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); +let mut result: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::zero(); test_size]); + +let cfg = VecOpsConfig::default(); +add_scalars(&a, &b, &mut result, &cfg).unwrap(); +``` + +### Subtraction of Scalars + +```rust +use icicle_bn254::curve::{ScalarCfg, ScalarField}; +use icicle_core::vec_ops::{sub_scalars}; + +let test_size = 1 << 18; + +let a: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); +let b: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); +let mut result: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::zero(); test_size]); + +let cfg = VecOpsConfig::default(); +sub_scalars(&a, &b, &mut result, &cfg).unwrap(); +``` + +### Multiplication of Scalars + +```rust +use icicle_bn254::curve::{ScalarCfg, ScalarField}; +use icicle_core::vec_ops::{mul_scalars}; + +let test_size = 1 << 18; + +let a: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(F::Config::generate_random(test_size)); +let ones: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::one(); test_size]); +let mut result: HostOrDeviceSlice<'_, ScalarField> = HostOrDeviceSlice::on_host(vec![F::zero(); test_size]); + +let cfg = VecOpsConfig::default(); +mul_scalars(&a, &ones, &mut result, &cfg).unwrap(); +``` + +## Vector Operations Configuration + +The `VecOpsConfig` struct encapsulates the settings for vector operations, including device context and operation modes. + +### `VecOpsConfig` + +Defines configuration parameters for vector operations. + +```rust +pub struct VecOpsConfig<'a> { + pub ctx: DeviceContext<'a>, + is_a_on_device: bool, + is_b_on_device: bool, + is_result_on_device: bool, + pub is_async: bool, +} +``` + +#### Fields + +- **`ctx: DeviceContext<'a>`**: Specifies the device context for the operation, including the device ID and memory pool. +- **`is_a_on_device`**: Indicates if the first operand vector resides in device memory. +- **`is_b_on_device`**: Indicates if the second operand vector resides in device memory. +- **`is_result_on_device`**: Specifies if the result vector should be stored in device memory. +- **`is_async`**: Enables asynchronous operation. If `true`, operations are non-blocking; otherwise, they block the current thread. + +### Default Configuration + +`VecOpsConfig` can be initialized with default settings tailored for a specific device: + +```rust +let cfg = VecOpsConfig::default(); +``` + +These are the default settings. + +```rust +impl<'a> Default for VecOpsConfig<'a> { + fn default() -> Self { + Self::default_for_device(DEFAULT_DEVICE_ID) + } +} + +impl<'a> VecOpsConfig<'a> { + pub fn default_for_device(device_id: usize) -> Self { + VecOpsConfig { + ctx: DeviceContext::default_for_device(device_id), + is_a_on_device: false, + is_b_on_device: false, + is_result_on_device: false, + is_async: false, + } + } +} +``` + +## Vector Operations + +Vector operations are implemented through the `VecOps` trait, providing methods for addition, subtraction, and multiplication of vectors. + +### `VecOps` Trait + +```rust +pub trait VecOps { + fn add( + a: &HostOrDeviceSlice, + b: &HostOrDeviceSlice, + result: &mut HostOrDeviceSlice, + cfg: &VecOpsConfig, + ) -> IcicleResult<()>; + + fn sub( + a: &HostOrDeviceSlice, + b: &HostOrDeviceSlice, + result: &mut HostOrDeviceSlice, + cfg: &VecOpsConfig, + ) -> IcicleResult<()>; + + fn mul( + a: &HostOrDeviceSlice, + b: &HostOrDeviceSlice, + result: &mut HostOrDeviceSlice, + cfg: &VecOpsConfig, + ) -> IcicleResult<()>; +} +``` + +#### Methods + +All operations are element-wise operations, and the results placed into the `result` param. These operations are not in place. + +- **`add`**: Computes the element-wise sum of two vectors. +- **`sub`**: Computes the element-wise difference between two vectors. +- **`mul`**: Performs element-wise multiplication of two vectors. + +## MatrixTranspose API Documentation + +This section describes the functionality of the `TransposeMatrix` function used for matrix transposition. + +The function takes a matrix represented as a 1D slice and transposes it, storing the result in another 1D slice. + +### Function + +```rust +pub fn transpose_matrix( + input: &HostOrDeviceSlice, + row_size: u32, + column_size: u32, + output: &mut HostOrDeviceSlice, + ctx: &DeviceContext, + on_device: bool, + is_async: bool, +) -> IcicleResult<()> +where + F: FieldImpl, + ::Config: VecOps +``` + +### Parameters + +- **`input`**: A slice representing the input matrix. The slice can be stored on either the host or the device. +- **`row_size`**: The number of rows in the input matrix. +- **`column_size`**: The number of columns in the input matrix. +- **`output`**: A mutable slice to store the transposed matrix. The slice can be stored on either the host or the device. +- **`ctx`**: A reference to the `DeviceContext`, which provides information about the device where the operation will be performed. +- **`on_device`**: A boolean flag indicating whether the inputs and outputs are on the device. +- **`is_async`**: A boolean flag indicating whether the operation should be performed asynchronously. + +### Return Value + +`Ok(())` if the operation is successful, or an `IcicleResult` error otherwise. + +### Example + +```rust +use icicle::HostOrDeviceSlice; +use icicle::DeviceContext; +use icicle::FieldImpl; +use icicle::VecOps; + +let input: HostOrDeviceSlice = // ...; +let mut output: HostOrDeviceSlice = // ...; +let ctx: DeviceContext = // ...; + +transpose_matrix(&input, 5, 4, &mut output, &ctx, true, false) + .expect("Failed to transpose matrix"); +``` + +The function takes a matrix represented as a 1D slice, transposes it, and stores the result in another 1D slice. The input and output slices can be stored on either the host or the device, and the operation can be performed synchronously or asynchronously. + +The function is generic and can work with any type `F` that implements the `FieldImpl` trait. The `::Config` type must also implement the `VecOps` trait, which provides the `transpose` method used to perform the actual transposition. + +The function returns an `IcicleResult<()>`, indicating whether the operation was successful or not. diff --git a/docs/versioned_docs/version-2.8.0/introduction.md b/docs/versioned_docs/version-2.8.0/introduction.md new file mode 100644 index 000000000..14cb3ae72 --- /dev/null +++ b/docs/versioned_docs/version-2.8.0/introduction.md @@ -0,0 +1,47 @@ +--- +slug: / +displayed_sidebar: GettingStartedSidebar +title: '' +--- + +# Welcome to Ingonyama's Developer Documentation + +Ingonyama is a next-generation semiconductor company, focusing on Zero-Knowledge Proof hardware acceleration. We build accelerators for advanced cryptography, unlocking real-time applications. Our focus is on democratizing access to compute intensive cryptography and making it accessible for developers to build on top of. + +Currently our flagship products are: + +- **ICICLE**: + [ICICLE](https://github.com/ingonyama-zk/icicle) is a fully featured GPU accelerated cryptography library for building ZK provers. ICICLE allows you to accelerate your existing ZK protocols in a matter of hours or implement your protocol from scratch on GPU. + +--- + +## Our current take on hardware acceleration + +We believe GPUs are as important for ZK as for AI. + +- GPUs are a perfect match for ZK compute - around 97% of ZK protocol runtime is parallel by nature. +- GPUs are simple for developers to use and scale compared to other hardware platforms. +- GPUs are extremely competitive in terms of power / performance and price (3x cheaper compared to FPGAs). +- GPUs are popular and readily available. + +For a more in-depth understanding on this topic we suggest you read [our article on the subject](https://www.ingonyama.com/blog/revisiting-paradigm-hardware-acceleration-for-zero-knowledge-proofs). + +Despite our current focus on GPUs we are still hard at work developing a ZPU (ZK Processing Unit), with the goal of offering a programmable hardware platform for ZK. To read more about ZPUs we suggest you read this [article](https://medium.com/@ingonyama/zpu-the-zero-knowledge-processing-unit-f886a48e00e0). + +## ICICLE + +[ICICLE](https://github.com/ingonyama-zk/icicle) is a cryptography library for ZK using GPUs. +ICICLE implements blazing fast cryptographic primitives such as EC operations, MSM, NTT, Poseidon hash and more on GPU. + +ICICLE is designed to be easy to use, developers don't have to touch a single line of CUDA code. Our Rust and Golang bindings allow your team to transition from CPU to GPU with minimal changes. + +Learn more about ICICLE and GPUs [here][ICICLE-OVERVIEW]. + +## Get in Touch + +If you have any questions, ideas, or are thinking of building something in this space, join the discussion on [Discord]. You can explore our code on [github](https://github.com/ingonyama-zk) or read some of [our research papers](https://github.com/ingonyama-zk/papers). + +Follow us on [Twitter](https://x.com/Ingo_zk) and [YouTube](https://www.youtube.com/@ingo_ZK) and sign up for our [mailing list](https://wkf.ms/3LKCbdj) to get our latest announcements. + +[ICICLE-OVERVIEW]: ./icicle/overview.md +[Discord]: https://discord.gg/6vYrE7waPj diff --git a/docs/versioned_docs/version-3.0.0/contributor-guide.md b/docs/versioned_docs/version-3.0.0/contributor-guide.md new file mode 100644 index 000000000..ffd82cd9c --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/contributor-guide.md @@ -0,0 +1,23 @@ +# Contributor's Guide + +We welcome all contributions with open arms. At Ingonyama we take a village approach, believing it takes many hands and minds to build a ecosystem. + +## Contributing to ICICLE + +- Make suggestions or report bugs via [GitHub issues](https://github.com/ingonyama-zk/icicle/issues) +- Contribute to the ICICLE by opening a [pull request](https://github.com/ingonyama-zk/icicle/pulls). +- Contribute to our [documentation](https://github.com/ingonyama-zk/icicle/tree/main/docs) and [examples](https://github.com/ingonyama-zk/icicle/tree/main/examples). +- Ask questions on Discord + +### Opening a pull request + +When opening a [pull request](https://github.com/ingonyama-zk/icicle/pulls) please keep the following in mind. + +- `Clear Purpose` - The pull request should solve a single issue and be clean of any unrelated changes. +- `Clear description` - If the pull request is for a new feature describe what you built, why you added it and how its best that we test it. For bug fixes please describe the issue and the solution. +- `Consistent style` - Rust and Golang code should be linted by the official linters (golang fmt and rust fmt) and maintain a proper style. For CUDA and C++ code we use [`clang-format`](https://github.com/ingonyama-zk/icicle/blob/main/.clang-format), [here](https://github.com/ingonyama-zk/icicle/blob/605c25f9d22135c54ac49683b710fe2ce06e2300/.github/workflows/main-format.yml#L46) you can see how we run it. +- `Minimal Tests` - please add test which cover basic usage of your changes . + +## Questions? + +Find us on [Discord](https://discord.gg/6vYrE7waPj). diff --git a/docs/versioned_docs/version-3.0.0/grants.md b/docs/versioned_docs/version-3.0.0/grants.md new file mode 100644 index 000000000..1647d5400 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/grants.md @@ -0,0 +1,23 @@ +# Ingonyama Grant programs + +Ingonyama understands the importance of supporting and fostering a vibrant community of researchers and builders to advance ZK. To encourage progress, we are not only developing in the open but also sharing resources with researchers and builders through various programs. + +## ICICLE ZK-GPU Ecosystem Grant + +Ingonyama invites researchers and practitioners to collaborate in advancing ZK acceleration. We are allocating $100,000 for grants to support this initiative. + +### Bounties & Grants + +Eligibility for grants includes: + +1. **Students**: Utilize ICICLE in your research. +2. **Performance Improvement**: Enhance the performance of accelerated primitives in ICICLE. +3. **Protocol Porting**: Migrate existing ZK protocols to ICICLE. +4. **New Primitives**: Contribute new primitives to ICICLE. +5. **Benchmarking**: Compare ZK benchmarks against ICICLE. + +## Contact + +For questions or submissions: [grants@ingonyama.com](mailto:grants@ingonyama.com) + +**Read the full article [here](https://www.ingonyama.com/blog/icicle-for-researchers-grants-challenges)** diff --git a/docs/versioned_docs/version-3.0.0/icicle/arch_overview.md b/docs/versioned_docs/version-3.0.0/icicle/arch_overview.md new file mode 100644 index 000000000..a45f96a9b --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/arch_overview.md @@ -0,0 +1,27 @@ +# Architecture Overview + + +ICICLE v3 is designed with flexibility and extensibility in mind, offering a robust framework that supports multiple compute backends and accommodates various cryptographic needs. This section provides an overview of ICICLE's architecture, highlighting its open and closed components, multi-device support, and extensibility. + +### Frontend and CPU Backend + +- **Frontend (FE):** The ICICLE frontend is open-source and designed to provide a unified API across different programming languages, including C++, Rust, and Go. This frontend abstracts the complexity of working with different backends, allowing developers to write backend-agnostic code that can be deployed across various platforms. +- **CPU Backend:** ICICLE includes an open-source CPU backend that allows for development and testing on standard hardware. This backend is ideal for prototyping and for environments where specialized hardware is not available. + +## CUDA Backend + +- **CUDA Backend:** ICICLE also includes a high-performance CUDA backend that is closed-source. This backend is optimized for NVIDIA GPUs and provides significant acceleration for cryptographic operations. +- **Installation and Licensing:** The CUDA backend needs to be downloaded and installed. Refer to the [installation guide](./install_cuda_backend.md) for detailed instructions. + +## Multi-Device Support + +- **Scalability:** ICICLE supports multi-device configurations, enabling the distribution of workloads across multiple GPUs or other hardware accelerators. This feature allows for scaling ZK proofs and other cryptographic operations across larger data centers or high-performance computing environments. + + +## Build Your Own Backend + +ICICLE is designed to be extensible, allowing developers to integrate new backends or customize existing ones to suit their specific needs. The architecture supports: + +- **Custom Backends:** Developers can create their own backends to leverage different hardware or optimize for specific use cases. The process of building and integrating a custom backend is documented in the [Build Your Own Backend](./build_your_own_backend.md) section. +- **Pluggable Components:** ICICLE's architecture allows for easy integration of additional cryptographic primitives or enhancements, ensuring that the framework can evolve with the latest advancements in cryptography and hardware acceleration. + diff --git a/docs/versioned_docs/version-3.0.0/icicle/benchmarks.md b/docs/versioned_docs/version-3.0.0/icicle/benchmarks.md new file mode 100644 index 000000000..4a9ee6a08 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/benchmarks.md @@ -0,0 +1,3 @@ +# Benchmarks + +TODO \ No newline at end of file diff --git a/docs/versioned_docs/version-3.0.0/icicle/build_from_source.md b/docs/versioned_docs/version-3.0.0/icicle/build_from_source.md new file mode 100644 index 000000000..c8f439506 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/build_from_source.md @@ -0,0 +1,179 @@ + +# Build ICICLE from source + +This guide will help you get started with building, testing, and installing ICICLE, whether you're using C++, Rust, or Go. It also covers installation of the CUDA backend and important build options. + +## Building and Testing ICICLE frontend + +### C++: Build, Test, and Install (Frontend) + +ICICLE can be built and tested in C++ using CMake. The build process is straightforward, but there are several flags you can use to customize the build for your needs. + +#### Build Commands + +1. **Clone the ICICLE repository:** + ```bash + git clone https://github.com/ingonyama-zk/icicle.git + cd icicle + ``` + +2. **Configure the build:** + ```bash + mkdir -p build && rm -rf build/* + cmake -S icicle -B build -DFIELD=babybear + ``` + +:::info +To specify the field, use the flag -DFIELD=field, where field can be one of the following: babybear, stark252, m31. + +To specify a curve, use the flag -DCURVE=curve, where curve can be one of the following: bn254, bls12_377, bls12_381, bw6_761, grumpkin. +::: + +:::tip +If you have access to cuda backend repo, it can be built along ICICLE frontend by adding the following to the cmake command +- `-DCUDA_BACKEND=local` # if you have it locally +- `-DCUDA_BACKEND=` # to pull CUDA backend, given you have access +::: + +3. **Build the project:** + ```bash + cmake --build build -j + ``` + This is building the [libicicle_device](./libraries.md#icicle-device) and the [libicicle_field_babybear](./libraries.md#icicle-core) frontend lib that correspond to the field or curve. + +4. **Link:** +Link you application (or library) to ICICLE: +```cmake +target_link_libraries(yourApp PRIVATE icicle_field_babybear icicle_device) +``` + +5. **Installation (optional):** +To install the libs, specify the install prefix in the [cmake command](./build_from_source.md#build-commands) +`-DCMAKE_INSTALL_PREFIX=/install/dir/`. Default install path on linux is `/usr/local` if not specified. For other systems it may differ. The cmake command will print it to the log +``` +-- CMAKE_INSTALL_PREFIX=/install/dir/for/cmake/install +``` +Then after building, use cmake to install the libraries: +``` +cmake -S icicle -B build -DFIELD=babybear -DCMAKE_INSTALL_PREFIX=/path/to/install/dir/ +cmake --build build -j # build +cmake --install build # install icicle to /path/to/install/dir/ +``` + +6. **Run tests (optional):** +Add `-DBUILD_TESTS=ON` to the [cmake command](./build_from_source.md#build-commands) and build. +Execute all tests +```bash +cmake -S icicle -B build -DFIELD=babybear -DBUILD_TESTS=ON +cmake --build build -j +cd build/tests +ctest +``` +or choose the test-suite +```bash +./build/tests/test_field_api # or another test suite +# can specify tests using regex. For example for tests with ntt in the name: +./build/tests/test_field_api --gtest_filter="*ntt*" +``` +:::note +Most tests assume a cuda backend exists and will fail otherwise if cannot find a CUDA device. +::: + +#### Build Flags + +You can customize your ICICLE build with the following flags: + +- `-DCPU_BACKEND=ON/OFF`: Enable or disable built-in CPU backend. `default=ON`. +- `-DCMAKE_INSTALL_PREFIX=/install/dir`: Specify install directory. `default=/usr/local`. +- `-DBUILD_TESTS=ON/OFF`: Enable or disable tests. `default=OFF`. +- `-DBUILD_BENCHMARKS=ON/OFF`: Enable or disable benchmarks. `default=OFF`. + +#### Features + +By default, all [features](./libraries.md#supported-curves-and-operations) are enabled. +This is since installed backends may implement and register all APIs. Missing APIs in the frontend would cause linkage to fail due to missing symbols. Therefore by default we include them in the frontend part too. + +To disable features, add the following to the cmake command. +- ntt: `-DNTT=OFF` +- msm: `-DMSM=OFF` +- g2 msm: `-DG2=OFF` +- ecntt: `-DECNTT=OFF` +- extension field: `-DEXT_FIELD=OFF` + +:::tip +Disabling features is useful when developing with a backend that is slow to compile (e.g. CUDA backend); +::: + +### Rust: Build, Test, and Install + +To build and test ICICLE in Rust, follow these steps: + +1. **Navigate to the Rust bindings directory:** +```bash +cd wrappers/rust # or go to a specific field/curve 'cd wrappers/rust/icicle-fields/icicle-babybear' +``` + +2. **Build the Rust project:** +```bash +cargo build --release +``` +By default, all [supported features are enabled](#features). +Cargo features are used to disable features, rather than enable them, for the reason explained [here](#features): +- `no_g2` to disable G2 MSM +- `no_ecntt` to disable ECNTT + +They can be disabled as follows: +```bash +cargo build --release --features=no_ecntt,no_g2 +``` + +:::note +If you have access to cuda backend repo, it can be built along ICICLE frontend by using the following cargo features: +- `cuda_backend` : if the cuda backend resides in `icicle/backend/cuda` +- `pull_cuda_backend` : to pull main branch and build it +::: + + +3. **Run tests:** +```bash +cargo test # optional: --features=no_ecntt,no_g2,cuda_backend +``` +:::note +Most tests assume a CUDA backend is installed and fail otherwise. +::: + +4. **Install the library:** + +By default, the libraries are installed to the `target//deps/icicle` dir. If you want them installed elsewhere, define the env variable: +```bash +export ICICLE_INSTALL_DIR=/path/to/install/dir +``` + +#### Use as cargo dependency +In cargo.toml, specify the ICICLE libs to use: + +```bash +[dependencies] +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-babybear = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +# add other ICICLE crates here if need additional fields/curves +``` + +Can specify `branch = ` or `tag = ` or `rev = `. + +To disable features: +```bash +icicle-bls12-377 = { git = "https://github.com/ingonyama-zk/icicle.git", features = ["no_g2"] } +``` + +As explained above, the libs will be built and installed to `target//deps/icicle` so you can easily link to them. Alternatively you can set `ICICLE_INSTALL_DIR` env variable for a custom install directory. + +:::warning +Make sure to install icicle libs when installing a library/application that depends on icicle such that it is located at runtime. +::: + +### Go: Build, Test, and Install (TODO) + +--- +**To install CUDA backend and license click [here](./install_cuda_backend.md#installation)** diff --git a/docs/versioned_docs/version-3.0.0/icicle/build_your_own_backend.md b/docs/versioned_docs/version-3.0.0/icicle/build_your_own_backend.md new file mode 100644 index 000000000..5cb2fc52a --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/build_your_own_backend.md @@ -0,0 +1,3 @@ +# Build Your Own Backend + +TODO \ No newline at end of file diff --git a/docs/versioned_docs/version-3.0.0/icicle/colab-instructions.md b/docs/versioned_docs/version-3.0.0/icicle/colab-instructions.md new file mode 100644 index 000000000..d642dce4b --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/colab-instructions.md @@ -0,0 +1,138 @@ +# Run ICICLE on Google Colab + +Google Colab lets you use a GPU free of charge, it's an Nvidia T4 GPU with 16 GB of memory, capable of running latest CUDA (tested on Cuda 12.2) +As Colab is able to interact with shell commands, a user can also install a framework and load git repositories into Colab space. + +## Prepare Colab environment + +First thing to do in a notebook is to set the runtime type to a T4 GPU. + +- in the upper corner click on the dropdown menu and select "change runtime type" + +![Change runtime](/img/colab_change_runtime.png) + +- In the window select "T4 GPU" and press Save + +![T4 GPU](/img/t4_gpu.png) + +Installing Rust is rather simple, just execute the following command: + +```sh +!apt install rustc cargo +``` + +To test the installation of Rust: + +```sh +!rustc --version +!cargo --version +``` + +A successful installation will result in a rustc and cargo version print, a faulty installation will look like this: + +```sh +/bin/bash: line 1: rustc: command not found +/bin/bash: line 1: cargo: command not found +``` + +Now we will check the environment: + +```sh +!nvcc --version +!gcc --version +!cmake --version +!nvidia-smi +``` + +A correct environment should print the result with no bash errors for `nvidia-smi` command and result in a **Teslt T4 GPU** type: + +```sh +nvcc: NVIDIA (R) Cuda compiler driver +Copyright (c) 2005-2023 NVIDIA Corporation +Built on Tue_Aug_15_22:02:13_PDT_2023 +Cuda compilation tools, release 12.2, V12.2.140 +Build cuda_12.2.r12.2/compiler.33191640_0 +gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 +Copyright (C) 2021 Free Software Foundation, Inc. +This is free software; see the source for copying conditions. There is NO +warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. + +cmake version 3.27.9 + +CMake suite maintained and supported by Kitware (kitware.com/cmake). +Wed Jan 17 13:10:18 2024 ++---------------------------------------------------------------------------------------+ +| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 | +|-----------------------------------------+----------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+======================+======================| +| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 | +| N/A 39C P8 9W / 70W | 0MiB / 15360MiB | 0% Default | +| | | N/A | ++-----------------------------------------+----------------------+----------------------+ + ++---------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=======================================================================================| +| No running processes found | ++---------------------------------------------------------------------------------------+ +``` + +## Cloning ICICLE and running test + +Now we are ready to clone ICICE repository, + +```sh +!git clone https://github.com/ingonyama-zk/icicle.git +``` + +We now can browse the repository and run tests to check the runtime environment: + +```sh +!ls -la +%cd icicle +``` + +Let's run a test! +Navigate to icicle/wrappers/rust/icicle-curves/icicle-bn254 and run cargo test: + +```sh +%cd wrappers/rust/icicle-curves/icicle-bn254/ +!cargo test --release +``` + +:::note + +Compiling the first time may take a while + +::: + +Test run should end like this: + +```sh +running 15 tests +test curve::tests::test_ark_point_convert ... ok +test curve::tests::test_ark_scalar_convert ... ok +test curve::tests::test_affine_projective_convert ... ok +test curve::tests::test_point_equality ... ok +test curve::tests::test_field_convert_montgomery ... ok +test curve::tests::test_scalar_equality ... ok +test curve::tests::test_points_convert_montgomery ... ok +test msm::tests::test_msm ... ok +test msm::tests::test_msm_skewed_distributions ... ok +test ntt::tests::test_ntt ... ok +test ntt::tests::test_ntt_arbitrary_coset ... ok +test msm::tests::test_msm_batch has been running for over 60 seconds +test msm::tests::test_msm_batch ... ok +test ntt::tests::test_ntt_coset_from_subgroup ... ok +test ntt::tests::test_ntt_device_async ... ok +test ntt::tests::test_ntt_batch ... ok + +test result: ok. 15 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 99.39s +``` + +Viola, ICICLE in Colab! diff --git a/docs/versioned_docs/version-3.0.0/icicle/faq_and_troubleshooting.md b/docs/versioned_docs/version-3.0.0/icicle/faq_and_troubleshooting.md new file mode 100644 index 000000000..79d07f5e6 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/faq_and_troubleshooting.md @@ -0,0 +1,9 @@ +# FAQ and troubleshooting + +## Frequently asked questions + +TODO + +## Troubleshooting + +TODO \ No newline at end of file diff --git a/docs/versioned_docs/version-3.0.0/icicle/getting_started.md b/docs/versioned_docs/version-3.0.0/icicle/getting_started.md new file mode 100644 index 000000000..3ca043aad --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/getting_started.md @@ -0,0 +1,168 @@ +# Getting started Guide + +## Overview + +This guide will walk you through the entire process of building, testing, and installing ICICLE using your preferred programming language—C++, Rust, or Go. Whether you're deploying on a CPU or leveraging CUDA for accelerated performance, this guide provides comprehensive instructions to get you started. It also outlines the typical workflow for a user, including key installation steps: + + +1. **Install ICICLE or build it from source**: This is explained in this guide. For building from source, refer to the [Build from Source page](./build_from_source.md). +2. **Follow the [Programmer’s Guide](./programmers_guide/general.md)**: Learn how to use ICICLE APIs. +3. **Start using ICICLE APIs on your CPU**: Your application will now use ICICLE on the CPU. +4. **Accelerate your application on a GPU**: [install the CUDA backend](./install_cuda_backend.md), load it, and select it in your application ([C++](./programmers_guide/cpp.md#loading-a-backend),[Rust](./programmers_guide/rust.md#loading-a-backend), [Go](./programmers_guide/go.md#loading-a-backend)). +5. **Run on the GPU**: Once the GPU backend is selected, all subsequent API calls will execute on the GPU. +6. **Optimize for multi-GPU environments**: Refer to the [Multi-GPU](./multi-device.md) Guide to fully utilize your system’s capabilities. +7. **Review memory management**: Revisit the [Memory Management section](./programmers_guide/general.md#device-abstraction) to allocate memory on the device efficiently and try to keep data on the GPU as long as possible. + + +The rest of this page details the content of a release, how to install it, and how to use it. ICICLE binaries are released for multiple Linux distributions, including Ubuntu 20.04, Ubuntu 22.04, RHEL 8, and RHEL 9. + +:::note +Future releases will also include support for macOS and other systems. +::: + +## Content of a Release + +Each ICICLE release includes a tar file named `icicle30-.tar.gz`, where `icicle30` indicates version 3.0. This tar file contains ICICLE frontend build artifacts and headers for a specific distribution. The tar file structure includes: + +- **`./icicle/include/`**: This directory contains all the necessary header files for using the ICICLE library from C++. +- **`./icicle/lib/`**: + - **Icicle Libraries**: All the core ICICLE libraries are located in this directory. Applications linking to ICICLE will use these libraries. + - **Backends**: The `./icicle/lib/backend/` directory houses backend libraries, including the CUDA backend (not included in this tar). + +- **CUDA backend** comes as separate tar `icicle30--cuda122.tar.gz` + - per distribution, for ICICLE-frontend v3.0 and CUDA 12.2. + +## Installing and using ICICLE + +- [Full C++ example](https://github.com/ingonyama-zk/icicle/tree/main/examples/c++/install-and-use-icicle) +- [Full Rust example](https://github.com/ingonyama-zk/icicle/tree/main/examples/rust/install-and-use-icicle) +- [Full Go example](https://github.com/ingonyama-zk/icicle/tree/main/examples/golang/install-and-use-icicle) + +1. **Extract and install the Tar Files**: + - [Download](https://github.com/ingonyama-zk/icicle/releases) the appropriate tar files for your distribution (Ubuntu 20.04, Ubuntu 22.04, or UBI 8,9 for RHEL compatible binaries). + - **Frontend libs and headers** should be installed in default search paths (such as `/usr/lib` and `usr/local/include`) for the compiler and linker to find. + - **Backend libs** should be installed in `/opt` + - Extract it to your desired location: + ```bash + # install the frontend part (Can skip for Rust) + tar xzvf icicle30-ubuntu22.tar.gz + cp -r ./icicle/lib/* /usr/lib/ + cp -r ./icicle/include/icicle/ /usr/local/include/ # copy C++ headers + # extract CUDA backend (OPTIONAL) + tar xzvf icicle30-ubuntu22-cuda122.tar.gz -C /opt + ``` + + :::note + Installing the frontend is optional for Rust. Rust does not use it. + ::: + + :::tip + You may install to any directory, but you need to ensure it can be found by the linker at compile and runtime. + You can install anywhere and use a symlink to ensure it can be easily found as if it were in the default directory. + ::: + +2. **Linking Your Application**: + + Applications need to link to the ICICLE device library and to every field and/or curve library. The backend libraries are dynamically loaded at runtime, so there is no need to link to them. + + **C++** + - When compiling your C++ application, link against the ICICLE libraries: + ```bash + g++ -o myapp myapp.cpp -licicle_device -licicle_field_bn254 -licicle_curve_bn254 + # if not installed in standard dirs, for example /custom/path/, need to specify it + g++ -o myapp myapp.cpp -I/custom/path/icicle/include -L/custom/path/icicle/lib -licicle_device -licicle_field_bn254 -licicle_curve_bn254 -Wl,-rpath,/custom/path/icicle/lib/ + ``` + + - Or via cmake + ```bash + # Add the executable + add_executable(example example.cpp) + # Link the libraries + target_link_libraries(example icicle_device icicle_field_bn254 icicle_curve_bn254) + + # OPTIONAL (if not installed in default location) + + # The following is setting compile and runtime paths for headers and libs assuming + # - headers in /custom/path/icicle/include + # - libs in/custom/path/icicle/lib + + # Include directories + target_include_directories(example PUBLIC /custom/path/icicle/include) + # Library directories + target_link_directories(example PUBLIC /custom/path/icicle/lib/) + # Set the RPATH so linker finds icicle libs at runtime + set_target_properties(example PROPERTIES + BUILD_RPATH /custom/path/icicle/lib/ + INSTALL_RPATH /custom/path/icicle/lib/) + ``` + + :::tip + If you face linkage issues, try `ldd myapp` to see the runtime dependencies. If ICICLE libs are not found, you need to add the install directory to the search path of the linker. In a development environment, you can do that using the environment variable export `LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/custom/path/icicle/lib` or similar (for non-Linux). For deployment, make sure it can be found and avoid using LD_LIBRARY_PATH. + + Alternatively, you can embed the search path in the app as an rpath by adding `-Wl,-rpath,/custom/path/icicle/lib/`. This is demonstrated above. + ::: + + **Rust** + - When building the ICICLE crates, ICICLE frontend libs are built from source, along with the Rust bindings. They are installed to `target//deps/icicle`, and Cargo will link them correctly. Note that you still need to install the CUDA backend if you have a CUDA GPU. + - Simply use `cargo build` or `cargo run` and it should link to ICICLE libs. + + **Go** - TODO + +:::warning +When deploying an application (whether in C++, Rust, or Go), you must make sure to either deploy the ICICLE libs (that you download or build from source) along with the application binaries (as tar, Docker image, package manager installer, or otherwise) or make sure to install ICICLE (and the backend) on the target machine. Otherwise, the target machine will have linkage issues. +::: + +## Backend Loading + +The ICICLE library dynamically loads backend libraries at runtime. By default, it searches for backends in the following order: + +1. **Environment Variable**: If the `ICICLE_BACKEND_INSTALL_DIR` environment variable is defined, ICICLE will prioritize this location. +2. **Default Directory**: If the environment variable is not set, Icicle will search in the default directory `/opt/icicle/lib/backend`. + +:::warning +If building ICICLE frontend from source, make sure to load a backend that is compatible with the frontend version. CUDA backend libs are forward compatible with newer frontends (e.g., CUDA-backend-3.0 works with ICICLE-3.2). The opposite is not guaranteed. +::: + +If you install in a custom dir, make sure to set `ICICLE_BACKEND_INSTALL_DIR`: +```bash +ICICLE_BACKEND_INSTALL_DIR=path/to/icicle/lib/backend/ myapp # for an executable myapp +ICICLE_BACKEND_INSTALL_DIR=path/to/icicle/lib/backend/ cargo run # when using cargo +``` + +Then to load backend from ICICLE_BACKEND_INSTALL_DIR or `/opt/icicle/lib/backend` in your application: + +**C++** +```cpp +extern "C" eIcicleError icicle_load_backend_from_env_or_default(); +``` +**Rust** +```rust +pub fn load_backend_from_env_or_default() -> Result<(), eIcicleError>; +``` +**Go** +```go +TODO +``` + +### Custom Backend Loading + +If you need to load a backend from a custom location at any point during runtime, you can call the following function: + +**C++** +```cpp +extern "C" eIcicleError icicle_load_backend(const char* path, bool is_recursive); +``` +- **`path`**: The directory where the backend libraries are located. +- **`is_recursive`**: If `true`, the function will search for backend libraries recursively within the specified path. + +**Rust** +```rust + pub fn load_backend(path: &str) -> Result<(), eIcicleError>; // OR + pub fn load_backend_non_recursive(path: &str) -> Result<(), eIcicleError>; +``` +- **`path`**: The directory where the backend libraries are located. + +**Go** +```go +TODO +``` diff --git a/docs/versioned_docs/version-3.0.0/icicle/golang-bindings.md b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings.md new file mode 100644 index 000000000..970797744 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings.md @@ -0,0 +1,148 @@ +# Golang bindings + +Golang bindings allow you to use ICICLE as a golang library. +The source code for all Golang packages can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang). + +The Golang bindings are comprised of multiple packages. + +[`core`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/core) which defines all shared methods and structures, such as configuration structures, or memory slices. + +[`runtime`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/runtime) which defines abstractions for ICICLE methods for allocating memory, initializing and managing streams, and `Device` which enables users to define and keep track of devices. + +Each supported curve and field has its own package which you can find in the respective directories [here](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang). If your project uses BN254 you only need to import that single package named [`bn254`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/curves/bn254). + +## Using ICICLE Golang bindings in your project + +To add ICICLE to your `go.mod` file. + +```bash +go get github.com/ingonyama-zk/icicle/v3 +``` + +If you want to specify a specific branch + +```bash +go get github.com/ingonyama-zk/icicle/v3@ +``` + +For a specific commit + +```bash +go get github.com/ingonyama-zk/icicle/v3@ +``` + +### Building from source + +To build the shared libraries you can run [this](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/build.sh) script inside the downloaded go dependency: + +```sh +./build.sh [-curve=] [-field=] [-cuda_version=] [-skip_msm] [-skip_ntt] [-skip_g2] [-skip_ecntt] [-skip_fieldext] + +curve - The name of the curve to build or "all" to build all supported curves +field - The name of the field to build or "all" to build all supported fields +-skip_msm - Optional - build with MSM disabled +-skip_ntt - Optional - build with NTT disabled +-skip_g2 - Optional - build with G2 disabled +-skip_ecntt - Optional - build with ECNTT disabled +-skip_fieldext - Optional - build without field extension +-help - Optional - Displays usage information +``` + +:::note + +If more than one curve or more than one field is supplied, the last one supplied will be built + +::: + +To build ICICLE libraries for all supported curves without certain features, you can use their -skip_\ flags. For example, for disabling G2 and ECNTT: + +```bash +./build.sh -curve=all -skip_g2 -skip_ecntt +``` + +By default, all features are enabled. To build for a specific field or curve, you can pass the `-field=` or `-curve=` flags: + +``` bash +./build.sh -curve=bn254 +``` + +Now you can import ICICLE into your project + +```go +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) +... +``` + +### Building with precompiled libs + +Download the frontend release binaries from our [github release page](https://github.com/ingonyama-zk/icicle/releases), for example: icicle30-ubuntu22.tar.gz for ICICLE v3 on ubuntu 22.04 + +Extract the libs and move them to the downloaded go dependency in your GOMODCACHE + +```sh +# extract frontend part +tar xzvf icicle30-ubuntu22.tar.gz +cp -r ./icicle/lib/* $(go env GOMODCACHE)/github.com/ingonyama-zk/icicle/v3@/build/lib/ +``` + +## Running tests + +To run all tests, for all curves: + +```bash +go test ./... -count=1 +``` + +If you wish to run test for a specific curve or field: + +```bash +go test -count=1 +``` + +## How do Golang bindings work? + +The golang packages are binded to the libraries produced from compiling ICICLE using cgo. + +1. These libraries (named `libicicle_curve_.a` and `libicicle_field_.a`) can be imported in your Go project to leverage the accelerated functionalities provided by ICICLE. + +2. In your Go project, you can use `cgo` to link these libraries. Here's a basic example on how you can use `cgo` to link these libraries: + +```go +/* +#cgo LDFLAGS: -L/path/to/shared/libs -licicle_device -lstdc++ -lm -Wl,-rpath=/path/to/shared/libs +#include "icicle.h" // make sure you use the correct header file(s) +*/ +import "C" + +func main() { + // Now you can call the C functions from the ICICLE libraries. + // Note that C function calls are prefixed with 'C.' in Go code. +} +``` + +Replace `/path/to/shared/libs` with the actual path where the shared libraries are located on your system. + +## Supported curves, fields and operations + +### Supported curves and operations + +| Operation\Curve | bn254 | bls12_377 | bls12_381 | bw6-761 | grumpkin | +| --------------- | :---: | :-------: | :-------: | :-----: | :------: | +| MSM | ✅ | ✅ | ✅ | ✅ | ✅ | +| G2 | ✅ | ✅ | ✅ | ✅ | ❌ | +| NTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| VecOps | ✅ | ✅ | ✅ | ✅ | ✅ | +| Polynomials | ✅ | ✅ | ✅ | ✅ | ❌ | + +### Supported fields and operations + +| Operation\Field | babybear | +| --------------- | :------: | +| VecOps | ✅ | +| Polynomials | ✅ | +| NTT | ✅ | +| Extension Field | ✅ | diff --git a/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/ecntt.md b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/ecntt.md new file mode 100644 index 000000000..4fd1acf8e --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/ecntt.md @@ -0,0 +1,100 @@ +# ECNTT + +## ECNTT Method + +The `ECNtt[T any]()` function performs the Elliptic Curve Number Theoretic Transform (EC-NTT) on the input points slice, using the provided dir (direction), cfg (configuration), and stores the results in the results slice. + +```go +func ECNtt[T any](points core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) runtime.EIcicleError +``` + +### Parameters + +- **`points`**: A slice of elliptic curve points (in projective coordinates) that will be transformed. The slice can be stored on the host or the device, as indicated by the `core.HostOrDeviceSlice` type. +- **`dir`**: The direction of the EC-NTT transform, either `core.KForward` or `core.KInverse`. +- **`cfg`**: A pointer to an `NTTConfig` object, containing configuration options for the NTT operation. +- **`results`**: A slice that will store the transformed elliptic curve points (in projective coordinates). The slice can be stored on the host or the device, as indicated by the `core.HostOrDeviceSlice` type. + +### Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the EC-NTT operation was successful, or an error if something went wrong. + +## NTT Configuration (NTTConfig) + +The `NTTConfig` structure holds configuration parameters for the NTT operation, allowing customization of its behavior to optimize performance based on the specifics of your protocol. + +```go +type NTTConfig[T any] struct { + StreamHandle runtime.Stream + CosetGen T + BatchSize int32 + ColumnsBatch bool + Ordering Ordering + areInputsOnDevice bool + areOutputsOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler +} +``` + +### Fields + +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`CosetGen`**: Coset generator. Used to perform coset (i)NTTs. +- **`BatchSize`**: The number of NTTs to compute in one operation, defaulting to 1. +- **`ColumnsBatch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. +- **`Ordering`**: Ordering of inputs and outputs (`KNN`, `KNR`, `KRN`, `KRR`), affecting how data is arranged. +- **`areInputsOnDevice`**: Indicates if input scalars are located on the device. +- **`areOutputsOnDevice`**: Indicates if results are stored on the device. +- **`IsAsync`**: Controls whether the NTT operation runs asynchronously. +- **`Ext`**: Extended configuration for backend. + +### Default Configuration + +Use `GetDefaultNTTConfig` to obtain a default configuration, customizable as needed. + +```go +func GetDefaultNTTConfig[T any](cosetGen T) NTTConfig[T] +``` + +## ECNTT Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ecntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func Main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + // Obtain the default NTT configuration with a predefined coset generator. + cfg := ntt.GetDefaultNttConfig() + + // Define the size of the input scalars. + size := 1 << 18 + + // Generate Points for the ECNTT operation. + points := bn254.GenerateProjectivePoints(size) + + // Set the direction of the NTT (forward or inverse). + dir := core.KForward + + // Allocate memory for the results of the NTT operation. + results := make(core.HostSlice[bn254.Projective], size) + + // Perform the NTT operation. + err := ecntt.ECNtt(points, dir, &cfg, results) + if err != runtime.Success { + panic("ECNTT operation failed") + } +} +``` diff --git a/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/msm-pre-computation.md b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/msm-pre-computation.md new file mode 100644 index 000000000..f13998d3e --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/msm-pre-computation.md @@ -0,0 +1,110 @@ +# MSM Pre computation + +To understand the theory behind MSM pre computation technique refer to Niall Emmart's [talk](https://youtu.be/KAWlySN7Hm8?feature=shared&t=1734). + +## Core package + +### MSM PrecomputeBases + +`PrecomputeBases` and `G2PrecomputeBases` exists for all supported curves. + +#### Description + +This function extends each provided base point $(P)$ with its multiples $(2^lP, 2^{2l}P, ..., 2^{(precompute_factor - 1) \cdot l}P)$, where $(l)$ is a level of precomputation determined by the `precompute_factor`. The extended set of points facilitates faster MSM computations by allowing the MSM algorithm to leverage precomputed multiples of base points, reducing the number of point additions required during the computation. + +The precomputation process is crucial for optimizing MSM operations, especially when dealing with large sets of points and scalars. By precomputing and storing multiples of the base points, the MSM function can more efficiently compute the scalar-point multiplications. + +#### `PrecomputeBases` + +Precomputes points for MSM by extending each base point with its multiples. + +```go +func PrecomputeBases(bases core.HostOrDeviceSlice, cfg *core.MSMConfig, outputBases core.DeviceSlice) runtime.EIcicleError +``` + +##### Parameters + +- **`bases`**: A slice of the original affine points to be extended with their multiples. +- **`cfg`**: The MSM configuration parameters. +- **`outputBases`**: The device slice allocated for storing the extended points. + +##### Example + +```go +package main + +import ( + "log" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + cfg := core.GetDefaultMSMConfig() + points := bn254.GenerateAffinePoints(1024) + cfg.PrecomputeFactor = 8 + var precomputeOut core.DeviceSlice + precomputeOut.Malloc(points[0].Size(), points.Len()*int(cfg.PrecomputeFactor)) + + err := msm.PrecomputeBases(points, &cfg, precomputeOut) + if err != runtime.Success { + log.Fatalf("PrecomputeBases failed: %v", err) + } +} +``` + +#### `G2PrecomputeBases` + +This method is the same as `PrecomputePoints` but for G2 points. Extends each G2 curve base point with its multiples for optimized MSM computations. + +```go +func G2PrecomputeBases(bases core.HostOrDeviceSlice, cfg *core.MSMConfig, outputBases core.DeviceSlice) runtime.EIcicleError +``` + +##### Parameters + +- **`bases`**: A slice of the original affine points to be extended with their multiples. +- **`cfg`**: The MSM configuration parameters. +- **`outputBases`**: The device slice allocated for storing the extended points. + +##### Example + +```go +package main + +import ( + "log" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/g2" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + cfg := core.GetDefaultMSMConfig() + points := g2.G2GenerateAffinePoints(1024) + cfg.PrecomputeFactor = 8 + var precomputeOut core.DeviceSlice + precomputeOut.Malloc(points[0].Size(), points.Len()*int(cfg.PrecomputeFactor)) + + err := g2.G2PrecomputeBases(points, &cfg, precomputeOut) + if err != runtime.Success { + log.Fatalf("PrecomputeBases failed: %v", err) + } +} +``` diff --git a/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/msm.md b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/msm.md new file mode 100644 index 000000000..7209eca47 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/msm.md @@ -0,0 +1,205 @@ +# MSM + +## MSM Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + // Obtain the default MSM configuration. + cfg := core.GetDefaultMSMConfig() + + // Define the size of the problem, here 2^18. + size := 1 << 18 + + // Generate scalars and points for the MSM operation. + scalars := bn254.GenerateScalars(size) + points := bn254.GenerateAffinePoints(size) + + // Create a CUDA stream for asynchronous operations. + stream, _ := runtime.CreateStream() + var p bn254.Projective + + // Allocate memory on the device for the result of the MSM operation. + var out core.DeviceSlice + _, e := out.MallocAsync(p.Size(), 1, stream) + + if e != runtime.Success { + panic(e) + } + + // Set the CUDA stream in the MSM configuration. + cfg.StreamHandle = stream + cfg.IsAsync = true + + // Perform the MSM operation. + e = msm.Msm(scalars, points, &cfg, out) + + if e != runtime.Success { + panic(e) + } + + // Allocate host memory for the results and copy the results from the device. + outHost := make(core.HostSlice[bn254.Projective], 1) + runtime.SynchronizeStream(stream) + runtime.DestroyStream(stream) + outHost.CopyFromDevice(&out) + + // Free the device memory allocated for the results. + out.Free() +} +``` + +## MSM Method + +```go +func Msm(scalars core.HostOrDeviceSlice, points core.HostOrDeviceSlice, cfg *core.MSMConfig, results core.HostOrDeviceSlice) runtime.EIcicleError +``` + +### Parameters + +- **`scalars`**: A slice containing the scalars for multiplication. It can reside either in host memory or device memory. +- **`points`**: A slice containing the points to be multiplied with scalars. Like scalars, these can also be in host or device memory. +- **`cfg`**: A pointer to an `MSMConfig` object, which contains various configuration options for the MSM operation. +- **`results`**: A slice where the results of the MSM operation will be stored. This slice can be in host or device memory. + +### Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. + +## MSMConfig + +The `MSMConfig` structure holds configuration parameters for the MSM operation, allowing customization of its behavior to optimize performance based on the specifics of the operation or the underlying hardware. + +```go +type MSMConfig struct { + StreamHandle runtime.Stream + PrecomputeFactor int32 + C int32 + Bitsize int32 + BatchSize int32 + ArePointsSharedInBatch bool + areScalarsOnDevice bool + AreScalarsMontgomeryForm bool + areBasesOnDevice bool + AreBasesMontgomeryForm bool + areResultsOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler +} +``` + +### Fields + +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`PrecomputeFactor`**: Controls the number of extra points to pre-compute. +- **`C`**: Window bitsize, a key parameter in the "bucket method" for MSM. +- **`Bitsize`**: Number of bits of the largest scalar. +- **`BatchSize`**: Number of results to compute in one batch. +- **`ArePointsSharedInBatch`**: Bases are shared for batch. Set to true if all MSMs use the same bases. Otherwise, the number of bases and number of scalars are expected to be equal. +- **`areScalarsOnDevice`**: Indicates if scalars are located on the device. +- **`AreScalarsMontgomeryForm`**: True if scalars are in Montgomery form. +- **`areBasesOnDevice`**: Indicates if bases are located on the device. +- **`AreBasesMontgomeryForm`**: True if point coordinates are in Montgomery form. +- **`areResultsOnDevice`**: Indicates if results are stored on the device. +- **`IsAsync`**: If true, runs MSM asynchronously. +- **`Ext`**: Extended configuration for backend. + +### Default Configuration + +Use `GetDefaultMSMConfig` to obtain a default configuration, which can then be customized as needed. + +```go +func GetDefaultMSMConfig() MSMConfig +``` + +## Batched msm + +For batch msm, simply allocate the results array with size corresponding to batch size and set the `ArePointsSharedInBatch` flag in config struct. + +```go +... + +// Obtain the default MSM configuration. +cfg := GetDefaultMSMConfig() + +cfg.Ctx.IsBigTriangle = true + +... +``` + +## How do I toggle between MSM modes? + +Toggling between MSM modes occurs automatically based on the number of results you are expecting from the `MSM` function. + +The number of results is interpreted from the size of `var out core.DeviceSlice`. Thus its important when allocating memory for `var out core.DeviceSlice` to make sure that you are allocating ` X `. + +```go +... + +batchSize := 3 +var p G2Projective +var out core.DeviceSlice +out.Malloc(p.Size(), batchSize) + +... +``` + +## Parameters for optimal performance + +Please refer to the [primitive description](../primitives/msm#choosing-optimal-parameters) + +## Support for G2 group + +To activate G2 support first you must make sure you are building the static libraries with G2 feature enabled as described in the [Golang building instructions](../golang-bindings.md#using-icicle-golang-bindings-in-your-project). + +Now you may import `g2` package of the specified curve. + +```go +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/g2" +) +``` + +This package include `G2Projective` and `G2Affine` points as well as a `G2Msm` method. + +```go +package main + +import ( + "log" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + cfg := core.GetDefaultMSMConfig() + points := bn254.GenerateAffinePoints(1024) + var precomputeFactor int32 = 8 + var precomputeOut core.DeviceSlice + precomputeOut.Malloc(points[0].Size(), points.Len()*int(precomputeFactor)) + + err := msm.PrecomputeBases(points, &cfg, precomputeOut) + if err != runtime.Success { + log.Fatalf("PrecomputeBases failed: %v", err) + } +} +``` + +`G2Msm` works the same way as normal MSM, the difference is that it uses G2 Points. diff --git a/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/multi-gpu.md b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/multi-gpu.md new file mode 100644 index 000000000..7f0a66257 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/multi-gpu.md @@ -0,0 +1,161 @@ +# Multi GPU APIs + +To learn more about the theory of Multi GPU programming refer to [this part](../multi-device.md) of documentation. + +Here we will cover the core multi GPU apis and an [example](#a-multi-gpu-example) + +## A Multi GPU example + +In this example we will display how you can + +1. Fetch the number of devices installed on a machine +2. For every GPU launch a thread and set an active device per thread. +3. Execute a MSM on each GPU + +```go +package main + +import ( + "fmt" + "sync" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + bn254 "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + + device := runtime.CreateDevice("CUDA", 0) + err := runtime.SetDevice(&device) + numDevices, _ := runtime.GetDeviceCount() + fmt.Println("There are ", numDevices, " devices available") + + if err != runtime.Success { + panic(err) + } + wg := sync.WaitGroup{} + + for i := 0; i < numDevices; i++ { + internalDevice := runtime.Device{DeviceType: device.DeviceType, Id: int32(i)} + wg.Add(1) + runtime.RunOnDevice(&internalDevice, func(args ...any) { + defer wg.Done() + currentDevice, err := runtime.GetActiveDevice() + if err != runtime.Success { + panic("Failed to get current device") + } + + fmt.Println("Running on ", currentDevice.GetDeviceType(), " ", currentDevice.Id, " device") + + cfg := msm.GetDefaultMSMConfig() + cfg.IsAsync = true + size := 1 << 10 + scalars := bn254.GenerateScalars(size) + points := bn254.GenerateAffinePoints(size) + + stream, _ := runtime.CreateStream() + var p bn254.Projective + var out core.DeviceSlice + _, err = out.MallocAsync(p.Size(), 1, stream) + if err != runtime.Success { + panic("Allocating bytes on device for Projective results failed") + } + cfg.StreamHandle = stream + + err = msm.Msm(scalars, points, &cfg, out) + if err != runtime.Success { + panic("Msm failed") + } + outHost := make(core.HostSlice[bn254.Projective], 1) + outHost.CopyFromDeviceAsync(&out, stream) + out.FreeAsync(stream) + + runtime.SynchronizeStream(stream) + runtime.DestroyStream(stream) + // Check with gnark-crypto + }) + } + wg.Wait() +} +``` + +This example demonstrates a basic pattern for distributing tasks across multiple GPUs. The `RunOnDevice` function ensures that each goroutine is executed on its designated GPU and a corresponding thread. + +## Device Management API + +To streamline device management we offer as part of `runtime` package methods for dealing with devices. + +### `RunOnDevice` + +Runs a given function on a specific GPU device, ensuring that all CUDA calls within the function are executed on the selected device. + +In Go, most concurrency can be done via Goroutines. However, there is no guarantee that a goroutine stays on a specific host thread. + +`RunOnDevice` was designed to solve this caveat and ensure that the goroutine will stay on a specific host thread. + +`RunOnDevice` locks a goroutine into a specific host thread, sets a current GPU device, runs a provided function, and unlocks the goroutine from the host thread after the provided function finishes. + +While the goroutine is locked to the host thread, the Go runtime will not assign other goroutines to that host thread. + +**Parameters:** + +- **`device *Device`**: A pointer to the `Device` instance to be used to run code. +- **`funcToRun func(args ...any)`**: The function to be executed on the specified device. +- **`args ...any`**: Arguments to be passed to `funcToRun`. + +**Behavior:** + +- The function `funcToRun` is executed in a new goroutine that is locked to a specific OS thread to ensure that all CUDA calls within the function target the specified device. + +:::note +Any goroutines launched within `funcToRun` are not automatically bound to the same GPU device. If necessary, `RunOnDevice` should be called again within such goroutines with the same `deviceId`. +::: + +**Example:** + +```go +device := runtime.CreateDevice("CUDA", 0) +RunOnDevice(&device, func(args ...any) { + fmt.Println("This runs on GPU 0") + // CUDA-related operations here will target GPU 0 +}, nil) +``` + +### `SetDevice` + +Sets the active device for the current host thread. All subsequent calls made from this thread will target the specified device. + +:::warning +This function should not be used directly in conjunction with goroutines. If you want to run multi-gpu scenarios with goroutines you should use [RunOnDevice](#runondevice) +::: + +**Parameters:** + +- **`device *Device`**: A pointer to the `Device` instance to be used to run code. + +**Returns:** + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. + +### `GetDeviceCount` + +Retrieves the number of devices available on the host. + +**Returns:** + +- **`(int, EIcicleError)`**: The number of devices and an error code indicating the success or failure of the operation. + +### `GetActiveDevice` + +Gets the device of the currently active device for the calling host thread. + +**Returns:** + +- **`(*Device, EIcicleError)`**: The device pointer and an error code indicating the success or failure of the operation. + + +This documentation should provide a clear understanding of how to effectively manage multiple GPUs in Go applications using CUDA and other backends, with a particular emphasis on the `RunOnDevice` function for executing tasks on specific GPUs. diff --git a/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/ntt.md b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/ntt.md new file mode 100644 index 000000000..9a947603e --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/ntt.md @@ -0,0 +1,140 @@ +# NTT + +## NTT Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + + "github.com/consensys/gnark-crypto/ecc/bn254/fr/fft" +) + +func init() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + cfg := core.GetDefaultNTTInitDomainConfig() + initDomain(18, cfg) +} + +func initDomain(largestTestSize int, cfg core.NTTInitDomainConfig) runtime.EIcicleError { + rouMont, _ := fft.Generator(uint64(1 << largestTestSize)) + rou := rouMont.Bits() + rouIcicle := bn254.ScalarField{} + limbs := core.ConvertUint64ArrToUint32Arr(rou[:]) + + rouIcicle.FromLimbs(limbs) + e := ntt.InitDomain(rouIcicle, cfg) + return e +} + +func main() { + // Obtain the default NTT configuration with a predefined coset generator. + cfg := ntt.GetDefaultNttConfig() + + // Define the size of the input scalars. + size := 1 << 18 + + // Generate scalars for the NTT operation. + scalars := bn254.GenerateScalars(size) + + // Set the direction of the NTT (forward or inverse). + dir := core.KForward + + // Allocate memory for the results of the NTT operation. + results := make(core.HostSlice[bn254.ScalarField], size) + + // Perform the NTT operation. + err := ntt.Ntt(scalars, dir, &cfg, results) + if err != runtime.Success { + panic("NTT operation failed") + } + + ntt.ReleaseDomain() +} +``` + +## NTT Method + +```go +func Ntt[T any](scalars core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) runtime.EIcicleError +``` + +### Parameters + +- **`scalars`**: A slice containing the input scalars for the transform. It can reside either in host memory or device memory. +- **`dir`**: The direction of the NTT operation (`KForward` or `KInverse`). +- **`cfg`**: A pointer to an `NTTConfig` object, containing configuration options for the NTT operation. +- **`results`**: A slice where the results of the NTT operation will be stored. This slice can be in host or device memory. + +### Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. + +## NTT Configuration (NTTConfig) + +The `NTTConfig` structure holds configuration parameters for the NTT operation, allowing customization of its behavior to optimize performance based on the specifics of your protocol. + +```go +type NTTConfig[T any] struct { + StreamHandle runtime.Stream + CosetGen T + BatchSize int32 + ColumnsBatch bool + Ordering Ordering + areInputsOnDevice bool + areOutputsOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler +} +``` + +### Fields + +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`CosetGen`**: Coset generator. Used to perform coset (i)NTTs. +- **`BatchSize`**: The number of NTTs to compute in one operation, defaulting to 1. +- **`ColumnsBatch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. +- **`Ordering`**: Ordering of inputs and outputs (`KNN`, `KNR`, `KRN`, `KRR`), affecting how data is arranged. +- **`areInputsOnDevice`**: Indicates if input scalars are located on the device. +- **`areOutputsOnDevice`**: Indicates if results are stored on the device. +- **`IsAsync`**: Controls whether the NTT operation runs asynchronously. +- **`Ext`**: Extended configuration for backend. + +### Default Configuration + +Use `GetDefaultNTTConfig` to obtain a default configuration, customizable as needed. + +```go +func GetDefaultNTTConfig[T any](cosetGen T) NTTConfig[T] +``` + +### Initializing the NTT Domain + +Before performing NTT operations, it's necessary to initialize the NTT domain; it only needs to be called once per GPU since the twiddles are cached. + +```go +func InitDomain(primitiveRoot bn254.ScalarField, cfg core.NTTInitDomainConfig) runtime.EIcicleError +``` + +This function initializes the domain with a given primitive root, optionally using fast twiddle factors to optimize the computation. + +### Releasing the domain + +The `ReleaseDomain` function is responsible for releasing the resources associated with a specific domain in the CUDA device context. + +```go +func ReleaseDomain() runtime.EIcicleError +``` + +### Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. diff --git a/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/vec-ops.md b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/vec-ops.md new file mode 100644 index 000000000..e93d9a0a2 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/golang-bindings/vec-ops.md @@ -0,0 +1,191 @@ +# Vector Operations + +## Overview + +Icicle exposes a number of vector operations which a user can use: + +* The VecOps API provides efficient vector operations such as addition, subtraction, and multiplication. +* MatrixTranspose API allows a user to perform a transpose on a vector representation of a matrix + +## VecOps API Documentation + +### Example + +#### Vector addition + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/vecOps" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + testSize := 1 << 12 + a := bn254.GenerateScalars(testSize) + b := bn254.GenerateScalars(testSize) + out := make(core.HostSlice[bn254.ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector multiplication + err := vecOps.VecOp(a, b, out, cfg, core.Add) + if err != runtime.Success { + panic("Vector addition failed") + } +} +``` + +#### Vector Subtraction + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/vecOps" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + testSize := 1 << 12 + a := bn254.GenerateScalars(testSize) + b := bn254.GenerateScalars(testSize) + out := make(core.HostSlice[bn254.ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector multiplication + err := vecOps.VecOp(a, b, out, cfg, core.Sub) + if err != runtime.Success { + panic("Vector subtraction failed") + } +} +``` + +#### Vector Multiplication + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/vecOps" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + testSize := 1 << 12 + a := bn254.GenerateScalars(testSize) + b := bn254.GenerateScalars(testSize) + out := make(core.HostSlice[bn254.ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector multiplication + err := vecOps.VecOp(a, b, out, cfg, core.Mul) + if err != runtime.Success { + panic("Vector multiplication failed") + } +} +``` + +### VecOps Method + +```go +func VecOp(a, b, out core.HostOrDeviceSlice, config core.VecOpsConfig, op core.VecOps) (ret runtime.EIcicleError) +``` + +#### Parameters + +- **`a`**: The first input vector. +- **`b`**: The second input vector. +- **`out`**: The output vector where the result of the operation will be stored. +- **`config`**: A `VecOpsConfig` object containing various configuration options for the vector operations. +- **`op`**: The operation to perform, specified as one of the constants (`Sub`, `Add`, `Mul`) from the `VecOps` type. + +#### Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. + +### VecOpsConfig + +The `VecOpsConfig` structure holds configuration parameters for the vector operations, allowing customization of its behavior. + +```go +type VecOpsConfig struct { + StreamHandle runtime.Stream + isAOnDevice bool + isBOnDevice bool + isResultOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler +} +``` + +#### Fields + +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`isAOnDevice`**: Indicates if vector `a` is located on the device. +- **`isBOnDevice`**: Indicates if vector `b` is located on the device. +- **`isResultOnDevice`**: Specifies where the result vector should be stored (device or host memory). +- **`IsAsync`**: Controls whether the vector operation runs asynchronously. +- **`Ext`**: Extended configuration for backend. + +#### Default Configuration + +Use `DefaultVecOpsConfig` to obtain a default configuration, customizable as needed. + +```go +func DefaultVecOpsConfig() VecOpsConfig +``` + +## MatrixTranspose API Documentation + +This section describes the functionality of the `TransposeMatrix` function used for matrix transposition. + +The function takes a matrix represented as a 1D slice and transposes it, storing the result in another 1D slice. + +### Function + +```go +func TransposeMatrix(in, out core.HostOrDeviceSlice, columnSize, rowSize int, config core.VecOpsConfig) runtime.EIcicleError +``` + +## Parameters + +- **`in`**: The input matrix is a `core.HostOrDeviceSlice`, stored as a 1D slice. +- **`out`**: The output matrix is a `core.HostOrDeviceSlice`, which will be the transpose of the input matrix, stored as a 1D slice. +- **`columnSize`**: The number of columns in the input matrix. +- **`rowSize`**: The number of rows in the input matrix. +- **`config`**: A `VecOpsConfig` object containing various configuration options for the vector operations. + +## Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. + +## Example Usage + +```go +var input = make(core.HostSlice[ScalarField], 20) +var output = make(core.HostSlice[ScalarField], 20) + +// Populate the input matrix +// ... + +// Get device context +cfg, _ := runtime.GetDefaultDeviceContext() + +// Transpose the matrix +err := TransposeMatrix(input, output, 5, 4, cfg) +if err != runtime.Success { + // Handle the error +} + +// Use the transposed matrix +// ... +``` + +In this example, the `TransposeMatrix` function is used to transpose a 5x4 matrix stored in a 1D slice. The input and output slices are stored on the host (CPU), and the operation is executed synchronously. diff --git a/docs/versioned_docs/version-3.0.0/icicle/install_cuda_backend.md b/docs/versioned_docs/version-3.0.0/icicle/install_cuda_backend.md new file mode 100644 index 000000000..f47dec7f0 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/install_cuda_backend.md @@ -0,0 +1,30 @@ + +# CUDA Backend + +## Overview + +The CUDA backend in ICICLE v3 is a high-performance, closed-source component designed to accelerate cryptographic computations using NVIDIA GPUs. This backend includes specialized libraries optimized for various cryptographic fields and curves, providing significant speedups for operations such as MSM, NTT, and elliptic curve operations. + +## Installation + +The CUDA backend is a closed-source component that requires a license. [To install the CUDA backend, see here](./getting_started#installing-and-using-icicle). + +### Licensing + +:::note +Currently, the CUDA backend is free to use via Ingonyama’s backend license server. By default, the CUDA backend will attempt to access this server. For more details, please contact support@ingonyama.com. +::: + +The CUDA backend requires a valid license to function. There are two types of CUDA backend licenses: + + 1. **Floating license**: In this mode, you host a license server, provided as a binary. This license supports a limited number of concurrent GPUs (N), which can be distributed across your machines as needed. N is decremented by 1 for each GPU using ICICLE per process. Once the process terminates (or crashes), the licenses are released. + 2. **Node locked license**: In this mode, the license is tied to a specific machine. The CUDA backend will accept the license only if it is used on the licensed machine. + +**To specify the license server address or file path::** + +``` +export ICICLE_LICENSE=port@ip # For license server +export ICICLE_LICENSE=/path/to/license # For node-locked license +``` + +For further assist , contact our support team for assistance support@ingonyama.com diff --git a/docs/versioned_docs/version-3.0.0/icicle/integrations.md b/docs/versioned_docs/version-3.0.0/icicle/integrations.md new file mode 100644 index 000000000..3a9e29cd5 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/integrations.md @@ -0,0 +1,97 @@ +# ICICLE integrated provers + +ICICLE has been used by companies and projects such as [Celer Network](https://github.com/celer-network), [Consensys Gnark](https://github.com/Consensys/gnark), [EZKL](https://blog.ezkl.xyz/post/acceleration/), [ZKWASM](https://twitter.com/DelphinusLab/status/1762604988797513915) and others to accelerate their ZK proving pipeline. + +Many of these integrations have been a collaboration between Ingonyama and the integrating company. We have learned a lot about designing GPU based ZK provers. + +If you're interested in understanding these integrations better or learning how you can use ICICLE to accelerate your existing ZK proving pipeline this is the place for you. + +## A primer to building your own integrations + +Lets illustrate an ICICLE integration, so you can understand the core API and design overview of ICICLE. + +![ICICLE architecture](/img/architecture-high-level.png) + +Engineers usually use a cryptographic library to implement their ZK protocols. These libraries implement efficient primitives which are used as building blocks for the protocol; ICICLE is such a library. The difference is that ICICLE is designed from the start to run on GPUs; the Rust and Golang APIs abstract away all low level CUDA details. Our goal was to allow developers with no GPU experience to quickly get started with ICICLE. + +A developer may use ICICLE with two main approaches in mind. + +1. Drop-in replacement approach. +2. End-to-End GPU replacement approach. + +The first approach for GPU-accelerating your Prover with ICICLE is quick to implement, but it has limitations, such as reduced memory optimization and limited protocol tuning for GPUs. It's a solid starting point, but those committed to fully leveraging GPU acceleration should consider a more comprehensive approach. + +A End-to-End GPU replacement means performing the entire ZK proof on the GPU. This approach will reduce latency to a minimum and requires you to change the way you implement the protocol to be more GPU friendly. This approach will take full advantage of GPU acceleration. Redesigning your prover this way may take more engineering effort but we promise you that its worth it! + +## Using ICICLE integrated provers + +Here we cover how a developer can run existing circuits on ICICLE integrated provers. + +### Gnark + +[Gnark](https://github.com/Consensys/gnark) officially supports GPU proving with ICICLE. Currently only Groth16 on curve `BN254` is supported. This means that if you are currently using Gnark to write your circuits you can enjoy GPU acceleration without making many changes. + +:::info + +Currently ICICLE has been merged to Gnark [master branch](https://github.com/Consensys/gnark), however the [latest release](https://github.com/Consensys/gnark/releases/tag/v0.9.1) is from October 2023. + +::: + +Make sure your golang circuit project has `gnark` as a dependency and that you are using the master branch for now. + +``` +go get github.com/consensys/gnark@master +``` + +You should see two indirect dependencies added. + +``` +... + github.com/ingonyama-zk/icicle v0.1.0 // indirect + github.com/ingonyama-zk/iciclegnark v0.1.1 // indirect +... +``` + +:::info +As you may notice we are using ICICLE v0.1 here since golang bindings are only support in ICICLE v0.1 for the time being. +::: + +To switch over to ICICLE proving, make sure to change the backend you are using, below is an example of how this should be done. + +``` +// toggle on +proofIci, err := groth16.Prove(ccs, pk, secretWitness, backend.WithIcicleAcceleration()) + +// toggle off +proof, err := groth16.Prove(ccs, pk, secretWitness) +``` + +Now that you have enabled `WithIcicleAcceleration` backend simple change the way your run your circuits to: + +``` +go run -tags=icicle main.go +``` + +Your logs should look something like this if everything went as expected. + +``` +13:12:05 INF compiling circuit +13:12:05 INF parsed circuit inputs nbPublic=1 nbSecret=1 +13:12:05 INF building constraint builder nbConstraints=3 +13:12:05 DBG precomputing proving key in GPU acceleration=icicle backend=groth16 curve=bn254 nbConstraints=3 +13:12:05 DBG constraint system solver done nbConstraints=3 took=0.070259 +13:12:05 DBG prover done acceleration=icicle backend=groth16 curve=bn254 nbConstraints=3 took=80.356684 +13:12:05 DBG verifier done backend=groth16 curve=bn254 took=1.843888 +``` + +`acceleration=icicle` indicates that the prover is running in acceleration mode with ICICLE. + +You can reference the [Gnark docs](https://github.com/Consensys/gnark?tab=readme-ov-file#gpu-support) for further information. + +### Halo2 + +[Halo2](https://github.com/zkonduit/halo2) fork integrated with ICICLE for GPU acceleration. This means that you can run your existing Halo2 circuits with GPU acceleration just by activating a feature flag. + +To enable GPU acceleration just enable `icicle_gpu` [feature flag](https://github.com/zkonduit/halo2/blob/3d7b5e61b3052680ccb279e05bdcc21dd8a8fedf/halo2_proofs/Cargo.toml#L102). + +This feature flag will seamlessly toggle on GPU acceleration for you. diff --git a/docs/versioned_docs/version-3.0.0/icicle/libraries.md b/docs/versioned_docs/version-3.0.0/icicle/libraries.md new file mode 100644 index 000000000..2a6a2f475 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/libraries.md @@ -0,0 +1,59 @@ +# ICICLE libraries + +ICICLE is composed of two main logical parts: +1. [**ICICLE device library**](#icicle-device) +2. [**ICICLE template core library**](#icicle-core) + +## ICICLE device + +The ICICLE device library serves as an abstraction layer for interacting with various hardware devices. It provides a comprehensive interface for tasks such as setting the active device, querying device-specific information like free and total memory, determining the number of available devices, and managing memory allocation. Additionally, it offers functionality for copying data to and from devices, managing task queues (streams) for efficient device utilization, and abstracting the complexities of device management away from the user. + +See programmers guide for more details. [C++](./programmers_guide/cpp#device-management), [Rust](./programmers_guide/rust#device-management), [Go](./programmers_guide/go) + +## ICICLE Core + +ICICLE Core is a template library written in C++ that implements fundamental cryptographic operations, including field and curve arithmetic, as well as higher-level APIs such as MSM and NTT. + +The Core can be [instantiated](./build_from_source) for different fields, curves, and other cryptographic components, allowing you to tailor it to your specific needs. You can link your application to one or more ICICLE libraries, depending on the requirements of your project. For example, you might only need the babybear library or a combination of babybear and a Merkle tree builder. + + +### Rust +Each library has a corresponding crate. See [programmers guide](./programmers_guide/general.md) for more details. + +### Supported curves, fields and operations + +#### Supported curves and operations + +| Operation\Curve | [bn254](https://neuromancer.sk/std/bn/bn254) | [bls12-377](https://neuromancer.sk/std/bls/BLS12-377) | [bls12-381](https://neuromancer.sk/std/bls/BLS12-381) | [bw6-761](https://eprint.iacr.org/2020/351) | grumpkin | +| --------------------------------------------------- | :------------------------------------------: | :---------------------------------------------------: | :---------------------------------------------------: | :-----------------------------------------: | :------: | +| [MSM](./primitives/msm) | ✅ | ✅ | ✅ | ✅ | ✅ | +| G2 MSM | ✅ | ✅ | ✅ | ✅ | ❌ | +| [NTT](./primitives/ntt) | ✅ | ✅ | ✅ | ✅ | ❌ | +| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| [Vector operations](./primitives/vec_ops) | ✅ | ✅ | ✅ | ✅ | ✅ | +| [Polynomials](./polynomials/overview) | ✅ | ✅ | ✅ | ✅ | ❌ | +| [Poseidon](primitives/poseidon) | ✅ | ✅ | ✅ | ✅ | ✅ | +| [Merkle Tree](primitives/poseidon#the-tree-builder) | ✅ | ✅ | ✅ | ✅ | ✅ | + +#### Supported fields and operations + +| Operation\Field | [babybear](https://eprint.iacr.org/2023/824.pdf) | [Stark252](https://docs.starknet.io/documentation/architecture_and_concepts/Cryptography/p-value/) | +| ----------------------------------------- | :----------------------------------------------: | :------------------------------------------------------------------------------------------------: | +| [Vector operations](./primitives/vec_ops) | ✅ | ✅ | +| [Polynomials](./polynomials/overview) | ✅ | ✅ | +| [NTT](primitives/ntt) | ✅ | ✅ | +| Extension Field | ✅ | ❌ | + +#### Supported hashes + +| Hash | Sizes | +| ------ | :------: | +| Keccak | 256, 512 | + +## Backend +Each backend must implement the device API interface. +Each backend may implement +- One or more ICICLE library. For example implement only bn254 curve. +- One or more APIs in this library. For example MSM only. + +See [CUDA backend](./install_cuda_backend.md) and [Build Your Own Backend](./build_your_own_backend.md) for more info about implementing a backend. diff --git a/docs/versioned_docs/version-3.0.0/icicle/migrate_from_v2.md b/docs/versioned_docs/version-3.0.0/icicle/migrate_from_v2.md new file mode 100644 index 000000000..acb2ff482 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/migrate_from_v2.md @@ -0,0 +1,93 @@ + +# Migration from ICICLE v2 to v3 + +ICICLE v3 introduces a unified interface for high-performance computing across various devices, extending the functionality that was previously limited to GPUs in Icicle V2. This guide will assist you in transitioning from ICICLE v2 to v3 by highlighting the key changes and providing examples for both C++ and Rust. + +## Key Conceptual Changes + +- **Device Independence**: n V2, Icicle was primarily designed for GPU computation, directly utilizing CUDA APIs. In V3, Icicle has evolved to support a broader range of computational devices, including CPUs, GPUs, and other accelerators. As a result, CUDA APIs have been replaced with device-agnostic Icicle APIs. + +- **Unified API**: The APIs are now standardized across all devices, ensuring consistent usage and reducing the complexity of managing different hardware backends. + +:::warning +When migrating from v2 to v3, it is important to note that, by default, your code now executes on the CPU. This contrasts with V2, which was exclusively a CUDA library. For details on installing and using CUDA GPUs, refer to the [CUDA backend guide](./install_cuda_backend.md). +::: + +## Migration Guide for C++ + +### Replacing CUDA APIs with Icicle APIs + +In ICICLE v3, CUDA-specific APIs have been replaced with Icicle APIs that are designed to be backend-agnostic. This allows your code to run on different devices without requiring modifications. + +- **Device Management**: Use ICICLE's device management APIs instead of CUDA-specific functions. For example, instead of `cudaSetDevice()`, you would use `icicle_set_device()`. + +- **Memory Management**: Replace CUDA memory management functions such as `cudaMalloc()` and `cudaFree()` with ICICLE's `icicle_malloc()` and `icicle_free()`. + +- **Stream Management**: Replace `cudaStream_t` with `icicleStreamHandle` and use ICICLE's stream management functions. + +For a detailed overview and examples, please refer to the [Icicle C++ Programmer's Guide](./programmers_guide/cpp.md) for full API details. + +### Example Migration + +**V2 (CUDA-specific):** +```cpp +cudaStream_t stream; +cudaStreamCreate(&stream); +void* device_ptr; +cudaMalloc(&device_ptr, 1024); +// Perform operations using CUDA APIs +cudaStreamDestroy(stream); +cudaFree(device_ptr); +``` + +**V3 (Device-agnostic):** +```cpp +icicleStreamHandle stream; +icicle_create_stream(&stream); +void* device_ptr; +icicle_malloc(&device_ptr, 1024); +// Perform operations using Icicle APIs +icicle_destroy_stream(stream); +icicle_free(device_ptr); +``` + +## Migration Guide for Rust + +### Replacing `icicle_cuda_runtime` with `icicle_runtime` + +In ICICLE v3, the `icicle_cuda_runtime` crate is replaced with the `icicle_runtime` crate. This change reflects the broader support for different devices beyond just CUDA-enabled GPUs. + +- **Device Management**: Use `icicle_runtime`'s device management functions instead of those in `icicle_cuda_runtime`. The `Device` struct remains central, but it's now part of a more generalized runtime. + +- **Memory Abstraction**: The `DeviceOrHostSlice` trait remains for memory abstraction, allowing seamless data handling between host and device. + +- **Stream Management**: Replace `CudaStream` with `IcicleStream`, which now supports broader device types. + +### Example Migration + +**V2 (`icicle_cuda_runtime`):** +```rust +use icicle_cuda_runtime::{CudaStream, DeviceVec, HostSlice}; + +let mut stream = CudaStream::create().unwrap(); +let mut device_memory = DeviceVec::cuda_malloc(1024).unwrap(); +// Perform operations using CudaStream and related APIs +stream.synchronize().unwrap(); +``` + +**V3 (`icicle_runtime`):** +```rust +use icicle_runtime::{IcicleStream, DeviceVec, HostSlice}; + +let mut stream = IcicleStream::create().unwrap(); +let mut device_memory = DeviceVec::device_malloc(1024).unwrap(); +// Perform operations using IcicleStream and related APIs +stream.synchronize().unwrap(); +``` + +### Other Considerations + +- **API Names**: While most API names remain consistent, they are now part of a more generalized runtime that can support multiple devices. Ensure that you update the crate imports and function calls accordingly. +- **Backend Loading**: Ensure that you are loading the appropriate backend using the `load_backend_from_env_or_default()` function, which is essential for setting up the runtime environment. + +For further details and examples, refer to the [Programmer's Guide](./programmers_guide/general.md). diff --git a/docs/versioned_docs/version-3.0.0/icicle/multi-device.md b/docs/versioned_docs/version-3.0.0/icicle/multi-device.md new file mode 100644 index 000000000..d73a10534 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/multi-device.md @@ -0,0 +1,77 @@ + +# Multi-Device with ICICLE + +:::info +If you are looking for the Multi-Device API documentation refer [here](./rust-bindings/multi-gpu.md) for Rust and [here](./golang-bindings/multi-gpu.md) for Golang. +::: + +One common challenge with Zero-Knowledge (ZK) computation is managing large input sizes. It's not uncommon to encounter circuits surpassing 2^25 constraints, which can push even the most advanced compute devices to their limits. To effectively scale and process such large circuits, leveraging multiple devices becomes a necessity. + +Multi-Device programming involves developing software to operate across multiple GPUs, CPUs, or other specialized hardware devices. Let's first explore different approaches to Multi-Device programming, then we will cover how ICICLE allows you to easily develop your ZK computations to run across many devices. + +## Approaches to Multi-Device Programming + +There are many [different strategies](https://github.com/NVIDIA/multi-gpu-programming-models) available for implementing multi-device systems, but they can generally be split into two categories: + +### Device Server Approach + +This approach typically involves a single or multiple CPUs managing threads that read from and write to multiple devices, such as GPUs, CPUs, or accelerators. You can think of it as a scaled-up HOST-Device model. + +![Multi-Device Server Approach](/img/image.png) + +This approach doesn't necessarily allow for tackling larger computation sizes, but it does enable the simultaneous computation of tasks that wouldn't fit on a single device. + +For example, if you needed to compute two MSMs of size 2^26 on a GPU with 16GB of VRAM, you would normally have to perform these tasks asynchronously. However, by doubling the number of GPUs or other devices in your system, you can now run these computations in parallel. + +### Inter-Device Approach + +This more sophisticated approach involves leveraging technologies such as [GPUDirect, NCCL, NVSHMEM](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-cwes1084/), NVLink, or other interconnect technologies to combine multiple devices and split a computation across different hardware. + +This approach requires redesigning the algorithm at the software level to be compatible with splitting tasks among devices. In some cases, to minimize latency, special inter-device connections can be installed on a server to allow direct communication between multiple devices. + +## Writing ICICLE Code for Multi-Device Setups + +Currently, ICICLE adopts a Device Server approach, where we assume you have a machine with multiple devices (GPUs, CPUs, etc.) and wish to run computations on each device. + +Each thread needs to set a device. Following API calls (including memory management and compute APIs) will execute on that device, for this thread. + +### C++ +```cpp +// Set the device to CPU +Device device = "CPU"; +icicle_set_device(device); +``` +### Rust +```rust +// Set the device to the first CUDA GPU (GPU-0) +let device = icicle_runtime::Device::new("CUDA", 0 /* =device_id*/); +icicle_runtime::set_device(&device).unwrap(); +``` + +### Go (TODO) + +## Best Practices + +- **Avoid Hardcoding Device IDs:** If you want your software to take full advantage of all available devices on a machine, use methods such as `icicle_get_device_count()` to support an arbitrary number of devices. +```cpp +/** + * @brief Checks if the specified device is available. + * + * @param dev The device to check for availability. + * @return eIcicleError Status of the device availability check. + * - `SUCCESS` if the device is available. + * - `INVALID_DEVICE` if the device is not available. + */ +extern "C" eIcicleError icicle_is_device_available(const Device& dev); + + /** + * @brief Get number of available devices active device for thread + * + + * @return eIcicleError Status of the device set + */ +extern "C" eIcicleError icicle_get_device_count(int& device_count /*OUT*/); +``` + +- **Launch One CPU Thread per Device:** To avoid [common errors](https://developer.nvidia.com/blog/cuda-pro-tip-always-set-current-device-avoid-multithreading-bugs/) and ensure code readability, we recommend creating a dedicated thread for each device. Within each CPU thread, you can launch as many tasks as you like for a device, as long as they all run on the same device ID. This will make your code more manageable, easier to read, and performant. +--- diff --git a/docs/versioned_docs/version-3.0.0/icicle/overview.md b/docs/versioned_docs/version-3.0.0/icicle/overview.md new file mode 100644 index 000000000..e479fbbd9 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/overview.md @@ -0,0 +1,79 @@ +--- +slug: /icicle/overview +title: ICICLE Overview +--- + +# ICICLE Overview + +

+ Untitled design (29) +

+ +## What is ICICLE? + +[ICICLE](https://github.com/ingonyama-zk/icicle) is a cryptography library designed to accelerate zero-knowledge proofs (ZKPs) using multiple compute backends, including GPUs, CPUs, and potentially other platforms. ICICLE's key strength lies in its ability to implement blazing-fast cryptographic primitives, enabling developers to significantly reduce proving times with minimal effort. + +## Key Features + +- **Acceleration of “zk” Math:** ICICLE provides optimized implementations for cryptographic primitives crucial to zero-knowledge proofs, such as elliptic curve operations, MSM, NTT, Poseidon hash, and more. +- **Set of Libraries:** ICICLE includes a comprehensive set of libraries supporting various fields, curves, and other cryptographic needs. +- **Cross-Language Support:** Available bindings for C++, Rust, Go, and potentially Python make ICICLE accessible across different development environments. +- **Backend Agnosticism:** Develop on CPU and deploy on various backends, including GPUs, specialized hardware, and other emerging platforms, depending on your project's needs. +- **Extensibility:** Designed for easy integration and extension, allowing you to build and deploy custom backends and cryptographic primitives. + +## Evolution from v2 to v3 + +Originally, ICICLE was focused solely on GPU acceleration. With the release of v3, ICICLE now supports multiple backends, making it more versatile and adaptable to different hardware environments. Whether you're leveraging the power of GPUs or exploring other compute platforms, ICICLE v3 is designed to fit your needs. + +## Who Uses ICICLE? + +ICICLE has been successfully integrated and used by leading ZK companies such as [Celer Network](https://github.com/celer-network), [Gnark](https://github.com/Consensys/gnark), and others to enhance their ZK proving pipelines. + +## Don't Have Access to a GPU? + +We understand that not all developers have access to GPUs, but this shouldn't limit your ability to develop with ICICLE. Here are some ways to gain access to GPUs. + +### Grants + +At Ingonyama, we are committed to accelerating progress in ZK and cryptography. If you're an engineer, developer, or academic researcher, we invite you to check out [our grant program](https://www.ingonyama.com/blog/icicle-for-researchers-grants-challenges). We can provide access to GPUs and even fund your research. + +### Google Colab + +Google Colab is a great platform to start experimenting with ICICLE instantly. It offers free access to NVIDIA T4 GPUs, which are more than sufficient for experimenting and prototyping with ICICLE. + +For a detailed guide on setting up Google Colab with ICICLE, refer to [this article](./colab-instructions.md). + +### Vast.ai + +[Vast.ai](https://vast.ai/) offers a global GPU marketplace where you can rent different types of GPUs by the hour at competitive prices. Whether you need on-demand or interruptible rentals, Vast.ai provides flexibility for various use cases. Learn more about their rental options [here](https://vast.ai/faq#rental-types). + +## What Can You Do with ICICLE? + +[ICICLE](https://github.com/ingonyama-zk/icicle) can be used similarly to any other cryptography library. Through various integrations, ICICLE has proven effective in multiple use cases: + +### Circuit Developers + +If you're a circuit developer facing bottlenecks, integrating ICICLE into your prover may solve performance issues. ICICLE is integrated into popular ZK provers like [Gnark](https://github.com/Consensys/gnark) and [Halo2](https://github.com/zkonduit/halo2), enabling immediate GPU acceleration without additional code changes. + +### Integrating into Existing ZK Provers + +ICICLE allows for selective acceleration of specific parts of your ZK prover, helping to address specific bottlenecks without requiring a complete rewrite of your prover. + +### Developing Your Own ZK Provers + +For those building ZK provers from the ground up, ICICLE is an ideal tool for creating optimized and scalable provers. The ability to scale across multiple machines within a data center is a key advantage when using ICICLE with GPUs. + +### Developing Proof of Concepts + +ICICLE is also well-suited for prototyping and developing small-scale projects. With bindings for Golang and Rust, you can easily create a library implementing specific cryptographic primitives, such as a KZG commitment library, using ICICLE. + +--- + +## Get Started with ICICLE + +Explore the full capabilities of ICICLE by diving into the [Architecture](./arch_overview.md), [Getting Started Guide](./getting_started.md) and the [Programmer's Guide](./programmers_guide/general.md) to learn how to integrate, deploy, and extend ICICLE across different backends. + +If you have any questions or need support, feel free to reach out on [Discord], [GitHub](https://github.com/ingonyama-zk) or [via support email][SupportEmail]. We're here to help you accelerate your ZK development with ICICLE. + +[Discord]: https://discord.gg/6vYrE7waPj +[SupportEmail]: mailto:support@ingonyama.com diff --git a/docs/versioned_docs/version-3.0.0/icicle/polynomials/ffi.uml b/docs/versioned_docs/version-3.0.0/icicle/polynomials/ffi.uml new file mode 100644 index 000000000..5d0a1e3c8 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/polynomials/ffi.uml @@ -0,0 +1,27 @@ +@startuml +skinparam componentStyle uml2 + +' Define Components +component "C++ Template\nComponent" as CppTemplate { + [Parameterizable Interface] +} +component "C API Wrapper\nComponent" as CApiWrapper { + [C API Interface] +} +component "Rust Code\nComponent" as RustCode { + [Macro Interface\n(Template Instantiation)] +} + +' Define Artifact +artifact "Static Library\n«artifact»" as StaticLib + +' Connections +CppTemplate -down-> CApiWrapper : Instantiates +CApiWrapper .down.> StaticLib : Compiles into +RustCode -left-> StaticLib : Links against\nand calls via FFI + +' Notes +note right of CppTemplate : Generic C++\ntemplate implementation +note right of CApiWrapper : Exposes C API for FFI\nto Rust/Go +note right of RustCode : Uses macros to\ninstantiate templates +@enduml diff --git a/docs/versioned_docs/version-3.0.0/icicle/polynomials/hw_backends.uml b/docs/versioned_docs/version-3.0.0/icicle/polynomials/hw_backends.uml new file mode 100644 index 000000000..bb96db092 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/polynomials/hw_backends.uml @@ -0,0 +1,86 @@ +@startuml + +' Define Interface for Polynomial Backend Operations +interface IPolynomialBackend { + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +' Define Interface for Polynomial Context (State Management) +interface IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +' PolynomialAPI now uses two strategies: Backend and Context +class PolynomialAPI { + -backendStrategy: IPolynomialBackend + -contextStrategy: IPolynomialContext + -setBackendStrategy(IPolynomialBackend) + -setContextStrategy(IPolynomialContext) + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +' Backend Implementations +class GPUPolynomialBackend implements IPolynomialBackend { + #gpuResources: Resource + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +class ZPUPolynomialBackend implements IPolynomialBackend { + #zpuResources: Resource + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +class TracerPolynomialBackend implements IPolynomialBackend { + #traceData: Data + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +' Context Implementations (Placeholder for actual implementation) +class GPUContext implements IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +class ZPUContext implements IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +class TracerContext implements IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +' Relationships +PolynomialAPI o-- IPolynomialBackend : uses +PolynomialAPI o-- IPolynomialContext : uses +@enduml diff --git a/docs/versioned_docs/version-3.0.0/icicle/polynomials/overview.md b/docs/versioned_docs/version-3.0.0/icicle/polynomials/overview.md new file mode 100644 index 000000000..881eeef88 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/polynomials/overview.md @@ -0,0 +1,376 @@ +# Polynomial API Overview + +:::note +Read our paper on the Polynomials API in ICICLE v2 by clicking [here](https://eprint.iacr.org/2024/973). +::: + +## Introduction + +The Polynomial API offers a robust framework for polynomial operations within a computational environment. It's designed for flexibility and efficiency, supporting a broad range of operations like arithmetic, evaluation, and manipulation, all while abstracting from the computation and storage specifics. This enables adaptability to various backend technologies, employing modern C++ practices. + +## Key Features + +### Backend Agnostic Architecture + +Our API is structured to be independent of any specific computational backend. While a CUDA backend is currently implemented, the architecture facilitates easy integration of additional backends. This capability allows users to perform polynomial operations without the need to tailor their code to specific hardware, enhancing code portability and scalability. + +### Templating in the Polynomial API + +The Polynomial API is designed with a templated structure to accommodate different data types for coefficients, the domain, and images. This flexibility allows the API to be adapted for various computational needs and types of data. + +```cpp +template +class Polynomial { + // Polynomial class definition +} +``` + +In this template: + +- **`Coeff`**: Represents the type of the coefficients of the polynomial. +- **`Domain`**: Specifies the type for the input values over which the polynomial is evaluated. By default, it is the same as the type of the coefficients but can be specified separately to accommodate different computational contexts. +- **`Image`**: Defines the type of the output values of the polynomial. This is typically the same as the coefficients. + +#### Default instantiation + +```cpp +extern template class Polynomial; +``` + +#### Extended use cases + +The templated nature of the Polynomial API also supports more complex scenarios. For example, coefficients and images could be points on an elliptic curve (EC points), which are useful in cryptographic applications and advanced algebraic structures. This approach allows the API to be extended easily to support new algebraic constructions without modifying the core implementation. + +### Supported Operations + +The Polynomial class encapsulates a polynomial, providing a variety of operations: + +- **Construction**: Create polynomials from coefficients or evaluations on roots-of-unity domains. +- **Arithmetic Operations**: Perform addition, subtraction, multiplication, and division. +- **Evaluation**: Directly evaluate polynomials at specific points or across a domain. +- **Manipulation**: Features like slicing polynomials, adding or subtracting monomials inplace, and computing polynomial degrees. +- **Memory Access**: Access internal states or obtain device-memory views of polynomials. + +## Usage + +This section outlines how to use the Polynomial API in C++. Bindings for Rust and Go are detailed under the Bindings sections. + +:::note +Make sure to set an ICICLE device prior to using the polynomial API. +::: + +### Construction + +Polynomials can be constructed from coefficients, from evaluations on roots-of-unity domains, or by cloning existing polynomials. + +```cpp +// Construction +static Polynomial from_coefficients(const Coeff* coefficients, uint64_t nof_coefficients); +static Polynomial from_rou_evaluations(const Image* evaluations, uint64_t nof_evaluations); +// Clone the polynomial +Polynomial clone() const; +``` + +Example: + +```cpp +auto p_from_coeffs = Polynomial_t::from_coefficients(coeff /* :scalar_t* */, nof_coeffs); +auto p_from_rou_evals = Polynomial_t::from_rou_evaluations(rou_evals /* :scalar_t* */, nof_evals); +auto p_cloned = p.clone(); // p_cloned and p do not share memory +``` + +:::note +The coefficients or evaluations may be allocated either on host or device memory. In both cases the memory is copied to the backend device. +::: + +### Arithmetic + +Constructed polynomials can be used for various arithmetic operations: + +```cpp +// Addition +Polynomial operator+(const Polynomial& rhs) const; +Polynomial& operator+=(const Polynomial& rhs); // inplace addition + +// Subtraction +Polynomial operator-(const Polynomial& rhs) const; + +// Multiplication +Polynomial operator*(const Polynomial& rhs) const; +Polynomial operator*(const Domain& scalar) const; // scalar multiplication + +// Division A(x) = B(x)Q(x) + R(x) +std::pair divide(const Polynomial& rhs) const; // returns (Q(x), R(x)) +Polynomial operator/(const Polynomial& rhs) const; // returns quotient Q(x) +Polynomial operator%(const Polynomial& rhs) const; // returns remainder R(x) +Polynomial divide_by_vanishing_polynomial(uint64_t degree) const; // sdivision by the vanishing polynomial V(x)=X^N-1 +``` + +#### Example + +Given polynomials A(x),B(x),C(x) and V(x) the vanishing polynomial. + +$$ +H(x)=\frac{A(x) \cdot B(x) - C(x)}{V(x)} \space where \space V(x) = X^{N}-1 +$$ + +```cpp +auto H = (A*B-C).divide_by_vanishing_polynomial(N); +``` + +### Evaluation + +Evaluate polynomials at arbitrary domain points, across a domain or on a roots-of-unity domain. + +```cpp +Image operator()(const Domain& x) const; // evaluate f(x) +void evaluate(const Domain* x, Image* evals /*OUT*/) const; +void evaluate_on_domain(Domain* domain, uint64_t size, Image* evals /*OUT*/) const; // caller allocates memory +void evaluate_on_rou_domain(uint64_t domain_log_size, Image* evals /*OUT*/) const; // caller allocate memory +``` + +Example: + +```cpp +Coeff x = rand(); +Image f_x = f(x); // evaluate f at x + +// evaluate f(x) on a domain +uint64_t domain_size = ...; +auto domain = /*build domain*/; // host or device memory +auto evaluations = std::make_unique(domain_size); // can be device memory too +f.evaluate_on_domain(domain, domain_size, evaluations); + +// evaluate f(x) on roots of unity domain +uint64_t domain_log_size = ...; +auto evaluations_rou_domain = std::make_unique(1 << domain_log_size); // can be device memory too +f.evaluate_on_rou_domain(domain_log_size, evaluations_rou_domain); +``` + +### Manipulations + +Beyond arithmetic, the API supports efficient polynomial manipulations: + +#### Monomials + +```cpp +// Monomial operations +Polynomial& add_monomial_inplace(Coeff monomial_coeff, uint64_t monomial = 0); +Polynomial& sub_monomial_inplace(Coeff monomial_coeff, uint64_t monomial = 0); +``` + +The ability to add or subtract monomials directly and in-place is an efficient way to manipualte polynomials. + +Example: + +```cpp +f.add_monomial_in_place(scalar_t::from(5)); // f(x) += 5 +f.sub_monomial_in_place(scalar_t::from(3), 8); // f(x) -= 3x^8 +``` + +#### Computing the degree of a Polynomial + +```cpp +// Degree computation +int64_t degree(); +``` + +The degree of a polynomial is a fundamental characteristic that describes the highest power of the variable in the polynomial expression with a non-zero coefficient. +The `degree()` function in the API returns the degree of the polynomial, corresponding to the highest exponent with a non-zero coefficient. + +- For the polynomial $f(x) = x^5 + 2x^3 + 4$, the degree is 5 because the highest power of $x$ with a non-zero coefficient is 5. +- For a scalar value such as a constant term (e.g., $f(x) = 7$, the degree is considered 0, as it corresponds to $x^0$. +- The degree of the zero polynomial, $f(x) = 0$, where there are no non-zero coefficients, is defined as -1. This special case often represents an "empty" or undefined state in many mathematical contexts. + +Example: + +```cpp +auto f = /*some expression*/; +auto degree_of_f = f.degree(); +``` + +#### Slicing + +```cpp +// Slicing and selecting even or odd components. +Polynomial slice(uint64_t offset, uint64_t stride, uint64_t size = 0 /*0 means take all elements*/); +Polynomial even(); +Polynomial odd(); +``` + +The Polynomial API provides methods for slicing polynomials and selecting specific components, such as even or odd indexed terms. Slicing allows extracting specific sections of a polynomial based on an offset, stride, and size. + +The following examples demonstrate folding a polynomial's even and odd parts and arbitrary slicing; + +```cpp +// folding a polynomials even and odd parts with randomness +auto x = rand(); +auto even = f.even(); +auto odd = f.odd(); +auto fold_poly = even + odd * x; + +// arbitrary slicing (first quarter) +auto first_quarter = f.slice(0 /*offset*/, 1 /*stride*/, f.degree()/4 /*size*/); +``` + +### Memory access (copy/view) + +Access to the polynomial's internal state can be vital for operations like commitment schemes or when more efficient custom operations are necessary. This can be done either by copying or viewing the polynomial + +#### Copying + +Copies the polynomial coefficients to either host or device allocated memory. + +:::note +Copying to host memory is backend agnostic while copying to device memory requires the memory to be allocated on the corresponding backend. +::: + +```cpp +Coeff get_coeff(uint64_t idx) const; // copy single coefficient to host +uint64_t copy_coeffs(Coeff* coeffs, uint64_t start_idx, uint64_t end_idx) const; +``` + +Example: + +```cpp +auto coeffs_device = /*allocate CUDA or host memory*/ +f.copy_coeffs(coeffs_device, 0/*start*/, f.degree()); + +MSMConfig cfg = msm::defaultMSMConfig(); +cfg.are_points_on_device = true; // assuming copy to device memory +auto rv = msm::MSM(coeffs_device, points, msm_size, cfg, results); +``` + +#### Views + +The Polynomial API supports efficient data handling through the use of memory views. These views provide direct access to the polynomial's internal state without the need to copy data. This feature is particularly useful for operations that require direct access to device memory, enhancing both performance and memory efficiency. + +##### What is a Memory View? + +A memory view is essentially a pointer to data stored in device memory. By providing a direct access pathway to the data, it eliminates the need for data duplication, thus conserving both time and system resources. This is especially beneficial in high-performance computing environments where data size and operation speed are critical factors. + +##### Applications of Memory Views + +Memory views are extremely versatile and can be employed in various computational contexts such as: + +- **Commitments**: Views can be used to commit polynomial states in cryptographic schemes, such as Multi-Scalar Multiplications (MSM). +- **External Computations**: They allow external functions or algorithms to utilize the polynomial's data directly, facilitating operations outside the core polynomial API. This is useful for custom operations that are not covered by the API. + +##### Obtaining and Using Views + +To create and use views within the Polynomial API, functions are provided to obtain pointers to both coefficients and evaluation data. Here’s how they are generally structured: + +```cpp +// Obtain a view of the polynomial's coefficients +std::tuple, uint64_t /*size*/, uint64_t /*device_id*/> get_coefficients_view(); +``` + +Example usage: + +```cpp +auto [coeffs_view, size, device_id] = polynomial.get_coefficients_view(); + +// Use coeffs_view in a computational routine that requires direct access to polynomial coefficients +// Example: Passing the view to a GPU-accelerated function +gpu_accelerated_function(coeffs_view.get(),...); +``` + +##### Integrity-Pointer: Managing Memory Views + +Within the Polynomial API, memory views are managed through a specialized tool called the Integrity-Pointer. This pointer type is designed to safeguard operations by monitoring the validity of the memory it points to. It can detect if the memory has been modified or released, thereby preventing unsafe access to stale or non-existent data. +The Integrity-Pointer not only acts as a regular pointer but also provides additional functionality to ensure the integrity of the data it references. Here are its key features: + +```cpp +// Checks whether the pointer is still considered valid +bool isValid() const; + +// Retrieves the raw pointer or nullptr if pointer is invalid +const T* get() const; + +// Dereferences the pointer. Throws exception if the pointer is invalid. +const T& operator*() const; + +//Provides access to the member of the pointed-to object. Throws exception if the pointer is invalid. +const T* operator->() const; +``` + +Consider the Following case: + +```cpp +auto [coeff_view, size, device] = f.get_coefficients_view(); + +// Use the coefficients view to perform external operations +commit_to_polynomial(coeff_view.get(), size); + +// Modification of the original polynomial +f += g; // Any operation that modifies 'f' potentially invalidates 'coeff_view' + +// Check if the view is still valid before using it further +if (coeff_view.isValid()) { + perform_additional_computation(coeff_view.get(), size); +} else { + handle_invalid_data(); +} +``` + + + +## Multi-GPU Support with CUDA Backend + +The Polynomial API includes comprehensive support for multi-GPU environments, a crucial feature for leveraging the full computational power of systems equipped with multiple NVIDIA GPUs. This capability is part of the API's CUDA backend, which is designed to efficiently manage polynomial computations across different GPUs. + +### Setting the CUDA Device + +Like other components of the icicle framework, the Polynomial API allows explicit setting of the current CUDA device: + +```cpp +icicle_set_device(devA); +``` + +This function sets the active CUDA device. All subsequent operations that allocate or deal with polynomial data will be performed on this device. + +### Allocation Consistency + +Polynomials are always allocated on the current CUDA device at the time of their creation. It is crucial to ensure that the device context is correctly set before initiating any operation that involves memory allocation: + +```cpp +// Set the device before creating polynomials +icicle_set_device(devA); +Polynomial p1 = Polynomial::from_coefficients(coeffs, size); + +icicle_set_device(devB); +Polynomial p2 = Polynomial::from_coefficients(coeffs, size); +``` + +### Matching Devices for Operations + +When performing operations that result in the creation of new polynomials (such as addition or multiplication), it is imperative that both operands are on the same CUDA device. If the operands reside on different devices, an exception is thrown: + +```cpp +// Ensure both operands are on the same device +icicle_set_device(devA); +auto p3 = p1 + p2; // Throws an exception if p1 and p2 are not on the same device +``` + +### Device-Agnostic Operations + +Operations that do not involve the creation of new polynomials, such as computing the degree of a polynomial or performing in-place modifications, can be executed regardless of the current device setting: + +```cpp +// 'degree' and in-place operations do not require device matching +int deg = p1.degree(); +p1 += p2; // Valid if p1 and p2 are on the same device, throws otherwise +``` + +### Error Handling + +The API is designed to throw exceptions if operations are attempted across polynomials that are not located on the same GPU. This ensures that all polynomial operations are performed consistently and without data integrity issues due to device mismatches. + +### Best Practices + +To maximize the performance and avoid runtime errors in a multi-GPU setup, always ensure that: + +- The CUDA device is set correctly before polynomial allocation. +- Operations involving new polynomial creation are performed with operands on the same device. + +By adhering to these guidelines, developers can effectively harness the power of multiple GPUs to handle large-scale polynomial computations efficiently. diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/Icicle_Release_README.md b/docs/versioned_docs/version-3.0.0/icicle/primitives/Icicle_Release_README.md new file mode 100644 index 000000000..510a4de7c --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/primitives/Icicle_Release_README.md @@ -0,0 +1,89 @@ + +# Icicle Release README + +## Overview + +Icicle is a powerful C++ library designed to provide flexible and efficient computation through its modular backend architecture. This README explains how to build and release Icicle for multiple Linux distributions, including Ubuntu 20.04, Ubuntu 22.04, and CentOS 7. It also describes the content of a release and how to use the generated tar files. + +## Content of a Release + +Each Icicle release includes a tar file containing the build artifacts for a specific distribution. The tar file includes the following structure: + +- **`./icicle/include/`**: This directory contains all the necessary header files for using the Icicle library from C++. + +- **`./icicle/lib/`**: + - **Icicle Libraries**: All the core Icicle libraries are located in this directory. Applications linking to Icicle will use these libraries. + - **Backends**: The `./icicle/lib/backend/` directory houses backend libraries, including the CUDA backend. While the CUDA backend is included, it will only be used on machines with a GPU. On machines without a GPU, the CUDA backend is not utilized. + +### Considerations + +Currently, the CUDA backend is included in every installation tar file, even on machines without a GPU. This ensures consistency across installations but results in additional files being installed that may not be used. + +## Build Docker Image + +To build the Docker images for each distribution and CUDA version, use the following commands: + +```bash +# Ubuntu 22.04, CUDA 12.2 +docker build -t icicle-release-ubuntu22-cuda122 -f Dockerfile.ubuntu22 . + +# Ubuntu 20.04, CUDA 12.2 +docker build -t icicle-release-ubuntu20-cuda122 -f Dockerfile.ubuntu20 . + +# CentOS 7, CUDA 12.2 +docker build -t icicle-release-centos7-cuda122 -f Dockerfile.centos7 . +``` + +### Docker Environment Explanation + +The Docker images you build represent the target environment for the release. Each Docker image is tailored to a specific distribution and CUDA version. You first build the Docker image, which sets up the environment, and then use this Docker image to build the release tar file. This ensures that the build process is consistent and reproducible across different environments. + +## Build Libraries Inside the Docker + +To build the Icicle libraries inside a Docker container and output the tar file to the `release_output` directory: + +```bash +mkdir -p release_output +docker run --rm --gpus all -v ./icicle:/icicle -v ./release_output:/output -v ./scripts:/scripts icicle-release-ubuntu22-cuda122 bash /scripts/release/build_release_and_tar.sh +``` + +This command executes the `build_release_and_tar.sh` script inside the Docker container, which provides the build environment. It maps the source code and output directory to the container, ensuring the generated tar file is available on the host system. + +You can replace `icicle-release-ubuntu22-cuda122` with another Docker image tag to build in the corresponding environment (e.g., Ubuntu 20.04 or CentOS 7). + +## Installing and Using the Release + +1. **Extract the Tar File**: + - Download the appropriate tar file for your distribution (Ubuntu 20.04, Ubuntu 22.04, or CentOS 7). + - Extract it to your desired location: + ```bash + tar -xzvf icicle--cuda122.tar.gz -C /path/to/install/location + ``` + +2. **Linking Your Application**: + - When compiling your C++ application, link against the Icicle libraries found in `./icicle/lib/`: + ```bash + g++ -o myapp myapp.cpp -L/path/to/icicle/lib -licicle_device -licicle_field_or_curve + ``` + - Note: You only need to link to the Icicle device and field or curve libraries. The backend libraries are dynamically loaded at runtime. + +## Backend Loading + +The Icicle library dynamically loads backend libraries at runtime. By default, it searches for backends in the following order: + +1. **Environment Variable**: If the `ICICLE_BACKEND_INSTALL_DIR` environment variable is defined, Icicle will prioritize this location. +2. **Default Directory**: If the environment variable is not set, Icicle will search in the default directory `/opt/icicle/lib/backend`. + +### Custom Backend Loading + +If you need to load a backend from a custom location at any point during runtime, you can call the following function: + +```cpp +extern "C" eIcicleError icicle_load_backend(const char* path, bool is_recursive); +``` + +- **`path`**: The directory where the backend libraries are located. +- **`is_recursive`**: If `true`, the function will search for backend libraries recursively within the specified path. + +--- + diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/image-1.png b/docs/versioned_docs/version-3.0.0/icicle/primitives/image-1.png new file mode 100644 index 000000000..cc0ba5916 Binary files /dev/null and b/docs/versioned_docs/version-3.0.0/icicle/primitives/image-1.png differ diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/image-2.png b/docs/versioned_docs/version-3.0.0/icicle/primitives/image-2.png new file mode 100644 index 000000000..e59a16925 Binary files /dev/null and b/docs/versioned_docs/version-3.0.0/icicle/primitives/image-2.png differ diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/image-3.png b/docs/versioned_docs/version-3.0.0/icicle/primitives/image-3.png new file mode 100644 index 000000000..75d3b4329 Binary files /dev/null and b/docs/versioned_docs/version-3.0.0/icicle/primitives/image-3.png differ diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/image.png b/docs/versioned_docs/version-3.0.0/icicle/primitives/image.png new file mode 100644 index 000000000..0fa4826e9 Binary files /dev/null and b/docs/versioned_docs/version-3.0.0/icicle/primitives/image.png differ diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/keccak.md b/docs/versioned_docs/version-3.0.0/icicle/primitives/keccak.md new file mode 100644 index 000000000..0d2a0dc90 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/primitives/keccak.md @@ -0,0 +1,77 @@ +# Keccak + +TODO update for V3 + +[Keccak](https://keccak.team/files/Keccak-implementation-3.2.pdf) is a cryptographic hash function designed by Guido Bertoni, Joan Daemen, Michaël Peeters, and Gilles Van Assche. It was selected as the winner of the NIST hash function competition, becoming the basis for the [SHA-3 standard](https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.202.pdf). + +Keccak operates on a message input of any length and produces a fixed-size hash output. The hash function is built upon the sponge construction, which involves absorbing the input data followed by squeezing out the hash value. + +At its core, Keccak consists of a permutation function operating on a state array. The permutation function employs a round function that operates iteratively on the state array. Each round consists of five main steps: + +- **Theta:** This step introduces diffusion by performing a bitwise XOR operation between the state and a linear combination of its neighboring columns. +- **Rho:** This step performs bit rotation operations on each lane of the state array. +- **Pi:** This step rearranges the positions of the lanes in the state array. +- **Chi:** This step applies a nonlinear mixing operation to each lane of the state array. +- **Iota:** This step introduces a round constant to the state array. + +## Using Keccak + +ICICLE Keccak supports batch hashing, which can be utilized for constructing a merkle tree or running multiple hashes in parallel. + +### Supported Bindings + +- [Golang](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/hash/keccak) +- [Rust](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/icicle-hash) + +### Example usage + +This is an example of running 1024 Keccak-256 hashes in parallel, where input strings are of size 136 bytes: + +```rust +use icicle_core::hash::HashConfig; +use icicle_cuda_runtime::memory::HostSlice; +use icicle_hash::keccak::keccak256; + +let config = HashConfig::default(); +let input_block_len = 136; +let number_of_hashes = 1024; + +let preimages = vec![1u8; number_of_hashes * input_block_len]; +let mut digests = vec![0u8; number_of_hashes * 64]; + +let preimages_slice = HostSlice::from_slice(&preimages); +let digests_slice = HostSlice::from_mut_slice(&mut digests); + +keccak256( + preimages_slice, + input_block_len as u32, + number_of_hashes as u32, + digests_slice, + &config, +) +.unwrap(); +``` + +### Merkle Tree + +You can build a keccak merkle tree using the corresponding functions: + +```rust +use icicle_core::tree::{merkle_tree_digests_len, TreeBuilderConfig}; +use icicle_cuda_runtime::memory::HostSlice; +use icicle_hash::keccak::build_keccak256_merkle_tree; + +let mut config = TreeBuilderConfig::default(); +config.arity = 2; +let height = 22; +let input_block_len = 136; +let leaves = vec![1u8; (1 << height) * input_block_len]; +let mut digests = vec![0u64; merkle_tree_digests_len((height + 1) as u32, 2, 1)]; + +let leaves_slice = HostSlice::from_slice(&leaves); +let digests_slice = HostSlice::from_mut_slice(&mut digests); + +build_keccak256_merkle_tree(leaves_slice, digests_slice, height, input_block_len, &config).unwrap(); +``` + +In the example above, a binary tree of height 22 is being built. Each leaf is considered to be a 136 byte long array. The leaves and digests are aligned in a flat array. You can also use keccak512 in `build_keccak512_merkle_tree` function. \ No newline at end of file diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/merkle.md b/docs/versioned_docs/version-3.0.0/icicle/primitives/merkle.md new file mode 100644 index 000000000..ab6b1badd --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/primitives/merkle.md @@ -0,0 +1,3 @@ +# Merkle tree + +TODO update for V3 \ No newline at end of file diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/msm.md b/docs/versioned_docs/version-3.0.0/icicle/primitives/msm.md new file mode 100644 index 000000000..32c6a5de7 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/primitives/msm.md @@ -0,0 +1,203 @@ + +# MSM - Multi Scalar Multiplication + +## Overview + +Multi-Scalar Multiplication (MSM) is a fundamental operation in elliptic curve cryptography and zero-knowledge proofs. It is defined as: + +$$ +MSM(a, G) = \sum_{j=0}^{n-1} a_j \cdot G_j +$$ + +Where: +- $G_j \in G$ are points from an elliptic curve group. +- $a_0, \ldots, a_n$ are scalars. +- $MSM(a, G) \in G$ is the result, a single elliptic curve point. + +MSM is inherently parallelizable, making it a critical operation for optimizing performance in cryptographic protocols like zk-SNARKs. Accelerating MSM can significantly reduce the time required for proof generation. + + +Accelerating MSM is crucial to a ZK protocol's performance due to the [large percent of run time](https://hackmd.io/@0xMonia/SkQ6-oRz3#Hardware-acceleration-in-action) they take when generating proofs. +You can learn more about how MSMs work from this [video](https://www.youtube.com/watch?v=Bl5mQA7UL2I) and from our resource list on [Ingopedia](https://www.ingonyama.com/ingopedia/msm). + +## C++ API + +### `MSMConfig` Struct + +The `MSMConfig` struct configures the MSM operation. It allows customization of parameters like the number of precomputed points, the window bitsize (`c`), and memory management. Here's the configuration structure: + +```cpp +struct MSMConfig { + icicleStreamHandle stream; + int precompute_factor; + int c; + int bitsize; + int batch_size; + bool are_points_shared_in_batch; + bool are_scalars_on_device; + bool are_scalars_montgomery_form; + bool are_points_on_device; + bool are_points_montgomery_form; + bool are_results_on_device; + bool is_async; + ConfigExtension* ext; +}; +``` + +#### Default Configuration + +You can obtain a default `MSMConfig` using: + +```cpp + static MSMConfig default_msm_config() + { + MSMConfig config = { + nullptr, // stream + 1, // precompute_factor + 0, // c + 0, // bitsize + 1, // batch_size + true, // are_points_shared_in_batch + false, // are_scalars_on_device + false, // are_scalars_montgomery_form + false, // are_points_on_device + false, // are_points_montgomery_form + false, // are_results_on_device + false, // is_async + nullptr, // ext + }; + return config; + } +``` + +### `msm` Function + +The `msm` function computes the MSM operation: + +```cpp +template +eIcicleError msm(const S* scalars, const A* bases, int msm_size, const MSMConfig& config, P* results); +``` + +:::note +The API is template and can work with all ICICLE curves (if corresponding lib is linked), including G2 groups. +::: + +### Batched MSM + +The MSM supports batch mode - running multiple MSMs in parallel. It's always better to use the batch mode instead of running single msms in serial as long as there is enough memory available. We support running a batch of MSMs that share the same points as well as a batch of MSMs that use different points. + +Config fields `are_points_shared_in_batch` and `batch_size` are used to configure msm for batch mode. + +### G2 MSM + +for G2 MSM, use the [same msm api](#msm-function) with the G2 types. + +:::note +Supported curves have types for both G1 and G2. +::: + +### Precompution + +#### What It Does: + +- The function computes a set of additional points derived from the original base points. These precomputed points are stored and later reused during the MSM computation. +- Purpose: By precomputing and storing these points, the MSM operation can reduce the number of operations needed at runtime, which can significantly speed up the calculation. + +#### When to Use: + +- Memory vs. Speed Trade-off: Precomputation increases the memory footprint because additional points are stored, but it reduces the computational effort during MSM, making the process faster. +- Best for Repeated Use: It’s especially beneficial when the same set of base points is used multiple times in different MSM operations. + +```cpp +template +eIcicleError msm_precompute_bases(const A* input_bases, int bases_size, const MSMConfig& config, A* output_bases); +``` + +:::note +User is allocating the `output_bases` (on host or device memory) and later use it as bases when calling msm. +::: + +## Rust and Go bindings + +The Rust and Go bindings provide equivalent functionality for their respective environments. Refer to their documentation for details on usage. + +- [Golang](../golang-bindings/msm.md) +- [Rust](../rust-bindings/msm.md) + +## CUDA backend MSM +This section describes the CUDA msm implementation and how to customize it (optional). + +### Algorithm description + +We follow the bucket method algorithm. The GPU implementation consists of four phases: + +1. Preparation phase - The scalars are split into smaller scalars of `c` bits each. These are the bucket indices. The points are grouped according to their corresponding bucket index and the buckets are sorted by size. +2. Accumulation phase - Each bucket accumulates all of its points using a single thread. More than one thread is assigned to large buckets, in proportion to their size. A bucket is considered large if its size is above the large bucket threshold that is determined by the `large_bucket_factor` parameter. The large bucket threshold is the expected average bucket size times the `large_bucket_factor` parameter. +3. Buckets Reduction phase - bucket results are multiplied by their corresponding bucket number and each bucket module is reduced to a small number of final results. By default, this is done by an iterative algorithm which is highly parallel. Setting `is_big_triangle` to `true` will switch this phase to the running sum algorithm described in the above YouTube talk which is much less parallel. +4. Final accumulation phase - The final results from the last phase are accumulated using the double-and-add algorithm. + +## Configuring CUDA msm +Use `ConfigExtension` object to pass backend specific configuration. +CUDA specific msm configuration: + +```cpp +ConfigExtension ext; +ext.set("large_bucket_factor", 15); +// use the config-extension in the msm config for the backend to see. +msm_config.ext = &ext; +// call msm +msm(..., config,...); // msm backend is reading the config-extension +``` + +### Choosing optimal parameters + +`is_big_triangle` should be `false` in almost all cases. It might provide better results only for very small MSMs (smaller than 2^8^) with a large batch (larger than 100) but this should be tested per scenario. +Large buckets exist in two cases: +1. When the scalar distribution isn't uniform. +2. When `c` does not divide the scalar bit-size. + +`large_bucket_factor` that is equal to 10 yields good results for most cases, but it's best to fine tune this parameter per `c` and per scalar distribution. +The two most important parameters for performance are `c` and the `precompute_factor`. They affect the number of EC additions as well as the memory size. When the points are not known in advance we cannot use precomputation. In this case the best `c` value is usually around $log_2(msmSize) - 4$. However, in most protocols the points are known in advanced and precomputation can be used unless limited by memory. Usually it's best to use maximum precomputation (such that we end up with only a single bucket module) combined we a `c` value around $log_2(msmSize) - 1$. + +## Memory usage estimation + +The main memory requirements of the MSM are the following: + +- Scalars - `sizeof(scalar_t) * msm_size * batch_size` +- Scalar indices - `~6 * sizeof(unsigned) * nof_bucket_modules * msm_size * batch_size` +- Points - `sizeof(affine_t) * msm_size * precomp_factor * batch_size` +- Buckets - `sizeof(projective_t) * nof_bucket_modules * 2^c * batch_size` + +where `nof_bucket_modules = ceil(ceil(bitsize / c) / precompute_factor)` + +During the MSM computation first the memory for scalars and scalar indices is allocated, then the indices are freed and points and buckets are allocated. This is why a good estimation for the required memory is the following formula: + +$max(scalars + scalarIndices, scalars + points + buckets)$ + +This gives a good approximation within 10% of the actual required memory for most cases. + +## Example parameters + +Here is a useful table showing optimal parameters for different MSMs. They are optimal for BLS12-377 curve when running on NVIDIA GeForce RTX 3090 Ti. This is the configuration used: + +Here are the parameters and the results for the different cases: + +| MSM size | Batch size | Precompute factor | c | Memory estimation (GB) | Actual memory (GB) | Single MSM time (ms) | +| -------- | ---------- | ----------------- | --- | ---------------------- | ------------------ | -------------------- | +| 10 | 1 | 1 | 9 | 0.00227 | 0.00277 | 9.2 | +| 10 | 1 | 23 | 11 | 0.00259 | 0.00272 | 1.76 | +| 10 | 1000 | 1 | 7 | 0.94 | 1.09 | 0.051 | +| 10 | 1000 | 23 | 11 | 2.59 | 2.74 | 0.025 | +| 15 | 1 | 1 | 11 | 0.011 | 0.019 | 9.9 | +| 15 | 1 | 16 | 16 | 0.061 | 0.065 | 2.4 | +| 15 | 100 | 1 | 11 | 1.91 | 1.92 | 0.84 | +| 15 | 100 | 19 | 14 | 6.32 | 6.61 | 0.56 | +| 18 | 1 | 1 | 14 | 0.128 | 0.128 | 14.4 | +| 18 | 1 | 15 | 17 | 0.40 | 0.42 | 5.9 | +| 22 | 1 | 1 | 17 | 1.64 | 1.65 | 68 | +| 22 | 1 | 13 | 21 | 5.67 | 5.94 | 54 | +| 24 | 1 | 1 | 18 | 6.58 | 6.61 | 232 | +| 24 | 1 | 7 | 21 | 12.4 | 13.4 | 199 | + +The optimal values can vary per GPU and per curve. It is best to try a few combinations until you get the best results for your specific case. diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/ntt.md b/docs/versioned_docs/version-3.0.0/icicle/primitives/ntt.md new file mode 100644 index 000000000..7d45ef9b1 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/primitives/ntt.md @@ -0,0 +1,322 @@ +# NTT - Number Theoretic Transform + +## Overview + +The Number Theoretic Transform (NTT) is a variant of the Fourier Transform used over finite fields, particularly those of integers modulo a prime number. NTT operates in a discrete domain and is used primarily in applications requiring modular arithmetic, such as cryptography and polynomial multiplication. + +NTT is defined similarly to the Discrete Fourier Transform (DFT), but instead of using complex roots of unity, it uses roots of unity within a finite field. The definition hinges on the properties of the finite field, specifically the existence of a primitive root of unity of order $N$ (where $N$ is typically a power of 2), and the modulo operation is performed with respect to a specific prime number that supports these roots. + +Formally, given a sequence of integers $a_0, a_1, ..., a_{N-1}$, the NTT of this sequence is another sequence of integers $A_0, A_1, ..., A_{N-1}$, computed as follows: + +$$ +A_k = \sum_{n=0}^{N-1} a_n \cdot \omega^{nk} \mod p +$$ + +where: + +- $N$ is the size of the input sequence and is a power of 2, +- $p$ is a prime number such that $p = kN + 1$ for some integer $k$, ensuring that $p$ supports the existence of $N$th roots of unity, +- $\omega$ is a primitive $N$th root of unity modulo $p$, meaning $\omega^N \equiv 1 \mod p$ and no smaller positive power of $\omega$ is congruent to 1 modulo $p$, +- $k$ ranges from 0 to $N-1$, and it indexes the output sequence. + +NTT is particularly useful because it enables efficient polynomial multiplication under modulo arithmetic, crucial for algorithms in cryptographic protocols and other areas requiring fast modular arithmetic operations. + +There exists also INTT which is the inverse operation of NTT. INTT can take as input an output sequence of integers from an NTT and reconstruct the original sequence. + +## C++ API + +### Ordering + +The `Ordering` enum defines how inputs and outputs are arranged for the NTT operation, offering flexibility in handling data according to different algorithmic needs or compatibility requirements. It primarily affects the sequencing of data points for the transform, which can influence both performance and the compatibility with certain algorithmic approaches. The available ordering options are: + +- **`kNN` (Natural-Natural):** Both inputs and outputs are in their natural order. This is the simplest form of ordering, where data is processed in the sequence it is given, without any rearrangement. + +- **`kNR` (Natural-Reversed):** Inputs are in natural order, while outputs are in bit-reversed order. This ordering is typically used in algorithms that benefit from having the output in a bit-reversed pattern. + +- **`kRN` (Reversed-Natural):** Inputs are in bit-reversed order, and outputs are in natural order. This is often used with the Cooley-Tukey FFT algorithm. + +- **`kRR` (Reversed-Reversed):** Both inputs and outputs are in bit-reversed order. + +- **`kNM` (Natural-Mixed):** Inputs are provided in their natural order, while outputs are arranged in a digit-reversed (mixed) order. This ordering is good for mixed radix NTT operations, where the mixed or digit-reversed ordering of outputs is a generalization of the bit-reversal pattern seen in simpler, radix-2 cases. + +- **`kMN` (Mixed-Natural):** Inputs are in a digit-reversed (mixed) order, while outputs are restored to their natural order. This ordering would primarily be used for mixed radix NTT + +Choosing an algorithm is heavily dependent on your use case. For example Cooley-Tukey will often use `kRN` and Gentleman-Sande often uses `kNR`. + +```cpp +enum class Ordering { + kNN, /**< Inputs and outputs are in natural-order. */ + kNR, /**< Inputs are in natural-order and outputs are in bit-reversed-order. */ + kRN, /**< Inputs are in bit-reversed-order and outputs are in natural-order. */ + kRR, /**< Inputs and outputs are in bit-reversed-order. */ + kNM, /**< Inputs are in natural-order and outputs are in digit-reversed-order. */ + kMN /**< Inputs are in digit-reversed-order and outputs are in natural-order. */ +}; +``` + +### `NTTConfig` Struct + +The `NTTConfig` struct configures the NTT operation. It allows customization of parameters like the batch size, column batch computation, order of inputs and outputs etc. + +```cpp + template + struct NTTConfig { + icicleStreamHandle stream; + S coset_gen; + int batch_size; + bool columns_batch; + Ordering ordering; + bool are_inputs_on_device; + bool are_outputs_on_device; + bool is_async; + ConfigExtension* ext = nullptr; + }; +``` + +#### Default configuration + +You can obtain a default `NTTConfig` using: +```cpp +template +static NTTConfig default_ntt_config() +{ + NTTConfig config = { + nullptr, // stream + S::one(), // coset_gen + 1, // batch_size + false, // columns_batch + Ordering::kNN, // ordering + false, // are_inputs_on_device + false, // are_outputs_on_device + false, // is_async + }; + return config; +} +``` + +### NTT domain +Before computing an NTT, it is mandatory to initialize the roots of unity domain for computing the NTT. + +:::note +NTT domain is constructed for a given size $2^N$ and can be used for any NTT of size smaller or equal to $2^N$. For example a domain of size 32 can be used to compute NTTs of size 2,4,8,16,32. +::: + +```cpp +template +eIcicleError ntt_init_domain(const S& primitive_root, const NTTInitDomainConfig& config); +``` + +:::note +Domain is constructed per device. When using multiple devices (e.g. GPUs), need to call it per device prior to calling ntt. +::: + +To retrieve a root of unity from the domain: +```cpp +template S get_root_of_unity(uint64_t max_size); +``` + +Finally, release the domain to free up device memory when not required: +```cpp +template S get_root_of_unity(uint64_t max_size); +``` + +where + +```cpp +struct NTTInitDomainConfig { + icicleStreamHandle stream; /**< Stream for asynchronous execution. */ + bool is_async; /**< True if operation is asynchronous. Default value is false. */ + ConfigExtension* ext = nullptr; /**< Backend-specific extensions. */ +}; + +static NTTInitDomainConfig default_ntt_init_domain_config() +{ + NTTInitDomainConfig config = { + nullptr, // stream + false // is_async + }; + return config; +} +``` + +### `ntt` Function + +the `ntt` function computes the NTT operation: + +```cpp +template +eIcicleError ntt(const E* input, int size, NTTDir dir, const NTTConfig& config, E* output); + +// Where NTTDir specific whether it is a forward or inverse transform +enum class NTTDir { + kForward, /**< Perform forward NTT. */ + kInverse /**< Perform inverse NTT (iNTT). */ +}; +``` +### EC-NTT +[The ntt api](#ntt-function) works for ECNTT too, given correct types, for supported curves. + +### Batch NTT + +Batch NTT allows you to compute many NTTs with a single API call. Batch NTT can significantly reduce read/write times as well as computation overhead by executing multiple NTT operations in parallel. Batch mode may also offer better utilization of computational resources (memory and compute). + +To compute a batch, set the `batch_size` and `columns_batch` fields of the config struct. + +### Rust and Go bindings + +- [Golang](../golang-bindings/ntt.md) +- [Rust](../rust-bindings/ntt.md) + +### Example + +The following example demonstartes how to use ntt and how pass custom configurations to the CUDA backend. This is discussed below. + +```cpp +#include "icicle/backend/ntt_config.h" + +// allocate and init input/output +int batch_size = /*...*/; +int log_ntt_size = /*...*/; +int ntt_size = 1 << log_ntt_size; +auto input = std::make_unique(batch_size * ntt_size); +auto output = std::make_unique(batch_size * ntt_size); +initialize_input(ntt_size, batch_size, input.get()); + +// Initialize NTT domain with fast twiddles (CUDA backend) +scalar_t basic_root = scalar_t::omega(log_ntt_size); +auto ntt_init_domain_cfg = default_ntt_init_domain_config(); +ConfigExtension backend_cfg_ext; +backend_cfg_ext.set(CudaBackendConfig::CUDA_NTT_FAST_TWIDDLES_MODE, true); +ntt_init_domain_cfg.ext = &backend_cfg_ext; +ntt_init_domain(basic_root, ntt_init_domain_cfg); + +// ntt configuration +NTTConfig config = default_ntt_config(); +ConfigExtension ntt_cfg_ext; +config.batch_size = batch_size; + +// Compute NTT with explicit selection of Mixed-Radix algorithm. +ntt_cfg_ext.set(CudaBackendConfig::CUDA_NTT_ALGORITHM, CudaBackendConfig::NttAlgorithm::MixedRadix); +config.ext = &ntt_cfg_ext; +ntt(input.get(), ntt_size, NTTDir::kForward, config, output.get()); +``` + +### CUDA backend NTT +This section describes the CUDA ntt implementation and how to use it. + +Our CUDA NTT implementation supports two algorithms `radix-2` and `mixed-radix`. + +### Radix 2 + +At its core, the Radix-2 NTT algorithm divides the problem into smaller sub-problems, leveraging the properties of "divide and conquer" to reduce the overall computational complexity. The algorithm operates on sequences whose lengths are powers of two. + +1. **Input Preparation:** + The input is a sequence of integers $a_0, a_1, \ldots, a_{N-1}, \text{ where } N$ is a power of two. + +2. **Recursive Decomposition:** + The algorithm recursively divides the input sequence into smaller sequences. At each step, it separates the sequence into even-indexed and odd-indexed elements, forming two subsequences that are then processed independently. + +3. **Butterfly Operations:** + The core computational element of the Radix-2 NTT is the "butterfly" operation, which combines pairs of elements from the sequences obtained in the decomposition step. + + Each butterfly operation involves multiplication by a "twiddle factor," which is a root of unity in the finite field, and addition or subtraction of the results, all performed modulo the prime modulus. + + $$ + X_k = (A_k + B_k \cdot W^k) \mod p + $$ + + $X_k$ - The output of the butterfly operation for the $k$-th element + + $A_k$ - an element from the even-indexed subset + + $B_k$ - an element from the odd-indexed subset + + $p$ - prime modulus + + $k$ - The index of the current operation within the butterfly or the transform stage + + The twiddle factors are precomputed to save runtime and improve performance. + +4. **Bit-Reversal Permutation:** + A final step involves rearranging the output sequence into the correct order. Due to the halving process in the decomposition steps, the elements of the transformed sequence are initially in a bit-reversed order. A bit-reversal permutation is applied to obtain the final sequence in natural order. + +### Mixed Radix + +The Mixed Radix NTT algorithm extends the concepts of the Radix-2 algorithm by allowing the decomposition of the input sequence based on various factors of its length. Specifically ICICLEs implementation splits the input into blocks of sizes 16, 32, or 64 compared to radix2 which is always splitting such that we end with NTT of size 2. This approach offers enhanced flexibility and efficiency, especially for input sizes that are composite numbers, by leveraging the "divide and conquer" strategy across multiple radices. + +The NTT blocks in Mixed Radix are implemented more efficiently based on winograd NTT but also optimized memory and register usage is better compared to Radix-2. + +Mixed Radix can reduce the number of stages required to compute for large inputs. + +1. **Input Preparation:** + The input to the Mixed Radix NTT is a sequence of integers $a_0, a_1, \ldots, a_{N-1}$, where $N$ is not strictly required to be a power of two. Instead, $N$ can be any composite number, ideally factorized into primes or powers of primes. + +2. **Factorization and Decomposition:** + Unlike the Radix-2 algorithm, which strictly divides the computational problem into halves, the Mixed Radix NTT algorithm implements a flexible decomposition approach which isn't limited to prime factorization. + + For example, an NTT of size 256 can be decomposed into two stages of $16 \times \text{NTT}_{16}$, leveraging a composite factorization strategy rather than decomposing into eight stages of $\text{NTT}_{2}$. This exemplifies the use of composite factors (in this case, $256 = 16 \times 16$) to apply smaller NTT transforms, optimizing computational efficiency by adapting the decomposition strategy to the specific structure of $N$. + +3. **Butterfly Operations with Multiple Radices:** + The Mixed Radix algorithm utilizes butterfly operations for various radix sizes. Each sub-transform involves specific butterfly operations characterized by multiplication with twiddle factors appropriate for the radix in question. + + The generalized butterfly operation for a radix-$r$ element can be expressed as: + + $$ + X_{k,r} = \sum_{j=0}^{r-1} (A_{j,k} \cdot W^{jk}) \mod p + $$ + + where: + + $X_{k,r}$ - is the output of the $radix-r$ butterfly operation for the $k-th$ set of inputs + + $A_{j,k}$ - represents the $j-th$ input element for the $k-th$ operation + + $W$ - is the twiddle factor + + $p$ - is the prime modulus + +4. **Recombination and Reordering:** + After applying the appropriate butterfly operations across all decomposition levels, the Mixed Radix algorithm recombines the results into a single output sequence. Due to the varied sizes of the sub-transforms, a more complex reordering process may be required compared to Radix-2. This involves digit-reversal permutations to ensure that the final output sequence is correctly ordered. + +### Which algorithm should I choose ? + +Both work only on inputs of power of 2 (e.g., 256, 512, 1024). + +Radix 2 is faster for small NTTs. A small NTT would be around logN = 16 and batch size 1. Radix 2 won't necessarily perform better for smaller `logn` with larger batches. + +Mixed radix on the other hand works better for larger NTTs with larger input sizes. + +Performance really depends on logn size, batch size, ordering, inverse, coset, coeff-field and which GPU you are using. + +For this reason we implemented our [heuristic auto-selection](https://github.com/ingonyama-zk/icicle/blob/main/icicle/src/ntt/ntt.cu#L573) which should choose the most efficient algorithm in most cases. + +We still recommend you benchmark for your specific use case if you think a different configuration would yield better results. + +To Explicitly choose the algorithm: + +```cpp +#include "icicle/backend/ntt_config.h" + +NTTConfig config = default_ntt_config(); +ConfigExtension ntt_cfg_ext; +ntt_cfg_ext.set(CudaBackendConfig::CUDA_NTT_ALGORITHM, CudaBackendConfig::NttAlgorithm::MixedRadix); +config.ext = &ntt_cfg_ext; +ntt(input.get(), ntt_size, NTTDir::kForward, config, output.get()); +``` + + +### Fast twiddles + +When using the Mixed-radix algorithm, it is recommended to initialize the domain in "fast-twiddles" mode. This is essentially allocating the domain using extra memory but enables faster ntt. +To do so simply, pass this flag to the CUDA backend. + +```cpp +#include "icicle/backend/ntt_config.h" + +scalar_t basic_root = scalar_t::omega(log_ntt_size); +auto ntt_init_domain_cfg = default_ntt_init_domain_config(); +ConfigExtension backend_cfg_ext; +backend_cfg_ext.set(CudaBackendConfig::CUDA_NTT_FAST_TWIDDLES_MODE, true); +ntt_init_domain_cfg.ext = &backend_cfg_ext; +ntt_init_domain(basic_root, ntt_init_domain_cfg); +``` \ No newline at end of file diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/overview.md b/docs/versioned_docs/version-3.0.0/icicle/primitives/overview.md new file mode 100644 index 000000000..fbbc0006e --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/primitives/overview.md @@ -0,0 +1,11 @@ +# ICICLE Compute APIs + +This section of the documentation is dedicated to the main APIs provided by ICICLE. We will cover the usage and internal details of our core primitives, such as Multi-Scalar Multiplication (MSM), Number Theoretic Transform (NTT), and various hashing algorithms. Each primitive has its own dedicated page with examples and explanations for C++, Rust, and Go. + +## Supported primitives + +- [MSM](./msm.md) +- [NTT](./ntt.md) +- [Vector Operations](./vec_ops.md) +- [Polynomials](../polynomials/overview.md) +- _Hashing and commitment schemes coming soon_ diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/poseidon.md b/docs/versioned_docs/version-3.0.0/icicle/primitives/poseidon.md new file mode 100644 index 000000000..37ea7455a --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/primitives/poseidon.md @@ -0,0 +1,218 @@ +# Poseidon + +TODO update for V3 + +[Poseidon](https://eprint.iacr.org/2019/458.pdf) is a popular hash in the ZK ecosystem primarily because its optimized to work over large prime fields, a common setting for ZK proofs, thereby minimizing the number of multiplicative operations required. + +Poseidon has also been specifically designed to be efficient when implemented within ZK circuits, Poseidon uses far less constraints compared to other hash functions like Keccak or SHA-256 in the context of ZK circuits. + +Poseidon has been used in many popular ZK protocols such as Filecoin and [Plonk](https://drive.google.com/file/d/1bZZvKMQHaZGA4L9eZhupQLyGINkkFG_b/view?usp=drive_open). + +Our implementation of Poseidon is implemented in accordance with the optimized [Filecoin version](https://spec.filecoin.io/algorithms/crypto/poseidon/). + +Lets understand how Poseidon works. + +## Initialization + +Poseidon starts with the initialization of its internal state, which is composed of the input elements and some pre-generated constants. An initial round constant is added to each element of the internal state. Adding the round constants ensures the state is properly mixed from the beginning. + +This is done to prevent collisions and to prevent certain cryptographic attacks by ensuring that the internal state is sufficiently mixed and unpredictable. + +![Poseidon initialization of internal state added with pre-generated round constants](https://github.com/ingonyama-zk/icicle/assets/122266060/52257f5d-6097-47c4-8f17-7b6449b9d162) + +## Applying full and partial rounds + +To generate a secure hash output, the algorithm goes through a series of "full rounds" and "partial rounds" as well as transformations between these sets of rounds in the following order: + +```First full rounds -> apply S-box and Round constants -> partial rounds -> Last full rounds -> Apply S-box``` + +### Full rounds + +![Full round iterations consisting of S box operations, adding round constants, and a Full MDS matrix multiplication](https://github.com/ingonyama-zk/icicle/assets/122266060/e4ce0e98-b90b-4261-b83e-3cd8cce069cb) + +**Uniform Application of S-box:** In full rounds, the S-box (a non-linear transformation) is applied uniformly to every element of the hash function's internal state. This ensures a high degree of mixing and diffusion, contributing to the hash function's security. The functions S-box involves raising each element of the state to a certain power denoted by `α` a member of the finite field defined by the prime `p`; `α` can be different depending on the implementation and user configuration. + +**Linear Transformation:** After applying the S-box, a linear transformation is performed on the state. This involves multiplying the state by a MDS (Maximum Distance Separable) Matrix. which further diffuses the transformations applied by the S-box across the entire state. + +**Addition of Round Constants:** Each element of the state is then modified by adding a unique round constant. These constants are different for each round and are precomputed as part of the hash function's initialization. The addition of round constants ensures that even minor changes to the input produce significant differences in the output. + +### Partial Rounds + +![Partial round iterations consisting of selective S box operation, adding a round constant and performing an MDS multiplication with a sparse matrix](https://github.com/ingonyama-zk/icicle/assets/122266060/e8c198b4-7aa4-4b4d-9ec4-604e39e07692) + +**Selective Application of S-Box:** Partial rounds apply the S-box transformation to only one element of the internal state per round, rather than to all elements. This selective application significantly reduces the computational complexity of the hash function without compromising its security. The choice of which element to apply the S-box to can follow a specific pattern or be fixed, depending on the design of the hash function. + +**Linear Transformation and Round Constants:** A linear transformation is performed and round constants are added. The linear transformation in partial rounds can be designed to be less computationally intensive (this is done by using a sparse matrix) than in full rounds, further optimizing the function's efficiency. + +The user of Poseidon can often choose how many partial or full rounds he wishes to apply; more full rounds will increase security but degrade performance. The choice and balance is highly dependent on the use case. + +## Using Poseidon + +ICICLE Poseidon is implemented for GPU and parallelization is performed for each element of the state rather than for each state. +What that means is we calculate multiple hash-sums over multiple pre-images in parallel, rather than going block by block over the input vector. + +So for Poseidon of arity 2 and input of size 1024 * 2, we would expect 1024 elements of output. Which means each block would be of size 2 and that would result in 1024 Poseidon hashes being performed. + +### Supported Bindings + +[`Go`](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/golang/curves/bn254/poseidon/poseidon.go) +[`Rust`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/icicle-core/src/poseidon) + +### Constants + +Poseidon is extremely customizable and using different constants will produce different hashes, security levels and performance results. + +We support pre-calculated and optimized constants for each of the [supported curves](../libraries#supported-curves-and-operations).The constants can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/poseidon/constants) and are labeled clearly per curve `_poseidon.h`. + +If you wish to generate your own constants you can use our python script which can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/poseidon/constants/generate_parameters.py). + +Prerequisites: + +- Install python 3 +- `pip install poseidon-hash` +- `pip install galois==0.3.7` +- `pip install numpy` + +You will then need to modify the following values before running the script. + +```python +# Modify these +arity = 11 # we support arity 2, 4, 8 and 11. +p = 0x73EDA753299D7D483339D80809A1D80553BDA402FFFE5BFEFFFFFFFF00000001 # bls12-381 +# p = 0x12ab655e9a2ca55660b44d1e5c37b00159aa76fed00000010a11800000000001 # bls12-377 +# p = 0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001 # bn254 +# p = 0x1ae3a4617c510eac63b05c06ca1493b1a22d9f300f5138f1ef3622fba094800170b5d44300000008508c00000000001 # bw6-761 +prime_bit_len = 255 +field_bytes = 32 + +... + +# primitive_element = None +primitive_element = 7 # bls12-381 +# primitive_element = 22 # bls12-377 +# primitive_element = 5 # bn254 +# primitive_element = 15 # bw6-761 +``` + +### Rust API + +This is the most basic way to use the Poseidon API. + +```rust +let test_size = 1 << 10; +let arity = 2u32; +let ctx = get_default_device_context(); +let poseidon = Poseidon::load(arity, &ctx).unwrap(); +let config = HashConfig::default(); + +let inputs = vec![F::one(); test_size * arity as usize]; +let outputs = vec![F::zero(); test_size]; +let mut input_slice = HostOrDeviceSlice::on_host(inputs); +let mut output_slice = HostOrDeviceSlice::on_host(outputs); + +poseidon.hash_many::( + &mut input_slice, + &mut output_slice, + test_size as u32, + arity as u32, + 1, // Output length + &config, +) +.unwrap(); +``` + +The `HashConfig` can be modified, by default the inputs and outputs are set to be on `Host` for example. + +```rust +impl<'a> Default for HashConfig<'a> { + fn default() -> Self { + let ctx = get_default_device_context(); + Self { + ctx, + are_inputs_on_device: false, + are_outputs_on_device: false, + is_async: false, + } + } +} +``` + +In the example above `Poseidon::load(arity, &ctx).unwrap();` is used which will load the correct constants based on arity and curve. Its possible to [generate](#constants) your own constants and load them. + +```rust +let ctx = get_default_device_context(); +let custom_poseidon = Poseidon::new( + arity, // The arity of poseidon hash. The width will be equal to arity + 1 + alpha, // The S-box power + full_rounds_half, + partial_rounds, + round_constants, + mds_matrix, + non_sparse_matrix, + sparse_matrices, + domain_tag, + ctx, +) +.unwrap(); +``` + +## The Tree Builder + +The tree builder allows you to build Merkle trees using Poseidon. + +You can define both the tree's `height` and its `arity`. The tree `height` determines the number of layers in the tree, including the root and the leaf layer. The `arity` determines how many children each internal node can have. + +```rust +use icicle_bn254::tree::Bn254TreeBuilder; +use icicle_bn254::poseidon::Poseidon; + +let mut config = TreeBuilderConfig::default(); +let arity = 2; +config.arity = arity as u32; +let input_block_len = arity; +let leaves = vec![F::one(); (1 << height) * arity]; +let mut digests = vec![F::zero(); merkle_tree_digests_len((height + 1) as u32, arity as u32, 1)]; + +let leaves_slice = HostSlice::from_slice(&leaves); +let digests_slice = HostSlice::from_mut_slice(&mut digests); + +let ctx = device_context::DeviceContext::default(); +let hash = Poseidon::load(2, &ctx).unwrap(); + +let mut config = TreeBuilderConfig::default(); +config.keep_rows = 5; +Bn254TreeBuilder::build_merkle_tree( + leaves_slice, + digests_slice, + height, + input_block_len, + &hash, + &hash, + &config, +) +.unwrap(); +``` + +Similar to Poseidon, you can also configure the Tree Builder `TreeBuilderConfig::default()` + +- `keep_rows`: The number of rows which will be written to output, 0 will write all rows. +- `are_inputs_on_device`: Have the inputs been loaded to device memory ? +- `is_async`: Should the TreeBuilder run asynchronously? `False` will block the current CPU thread. `True` will require you call `cudaStreamSynchronize` or `cudaDeviceSynchronize` to retrieve the result. + +### Benchmarks + +We ran the Poseidon tree builder on: + +**CPU**: 12th Gen Intel(R) Core(TM) i9-12900K/ + +**GPU**: RTX 3090 Ti + +**Tree height**: 30 (2^29 elements) + +The benchmarks include copying data from and to the device. + +| Rows to keep parameter | Run time, Icicle | Supranational PC2 +| ----------- | ----------- | ----------- +| 10 | 9.4 seconds | 13.6 seconds +| 20 | 9.5 seconds | 13.6 seconds +| 29 | 13.7 seconds | 13.6 seconds diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/poseidon2.md b/docs/versioned_docs/version-3.0.0/icicle/primitives/poseidon2.md new file mode 100644 index 000000000..622fc2a85 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/primitives/poseidon2.md @@ -0,0 +1,90 @@ +# Poseidon2 + +TODO update for V3 + +[Poseidon2](https://eprint.iacr.org/2023/323) is a recently released optimized version of Poseidon1. The two versions differ in two crucial points. First, Poseidon is a sponge hash function, while Poseidon2 can be either a sponge or a compression function depending on the use case. Secondly, Poseidon2 is instantiated by new and more efficient linear layers with respect to Poseidon. These changes decrease the number of multiplications in the linear layer by up to 90% and the number of constraints in Plonk circuits by up to 70%. This makes Poseidon2 currently the fastest arithmetization-oriented hash function without lookups. + + +## Using Poseidon2 + +ICICLE Poseidon2 is implemented for GPU and parallelization is performed for each state. +We calculate multiple hash-sums over multiple pre-images in parallel, rather than going block by block over the input vector. + +For example, for Poseidon2 of width 16, input rate 8, output elements 8 and input of size 1024 * 8, we would expect 1024 * 8 elements of output. Which means each input block would be of size 8, resulting in 1024 Poseidon2 hashes being performed. + +### Supported Bindings + +[`Rust`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/icicle-core/src/poseidon2) + +### Constants + +Poseidon2 is also extremely customizable and using different constants will produce different hashes, security levels and performance results. + +We support pre-calculated constants for each of the [supported curves](../libraries#supported-curves-and-operations). The constants can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/poseidon2/constants) and are labeled clearly per curve `_poseidon2.h`. + +You can also use your own set of constants as shown [here](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-fields/icicle-babybear/src/poseidon2/mod.rs#L290) + +### Rust API + +This is the most basic way to use the Poseidon2 API. + +```rust +let test_size = 1 << 10; +let width = 16; +let rate = 8; +let ctx = get_default_device_context(); +let poseidon = Poseidon2::load(width, rate, MdsType::Default, DiffusionStrategy::Default, &ctx).unwrap(); +let config = HashConfig::default(); + +let inputs = vec![F::one(); test_size * rate as usize]; +let outputs = vec![F::zero(); test_size]; +let mut input_slice = HostOrDeviceSlice::on_host(inputs); +let mut output_slice = HostOrDeviceSlice::on_host(outputs); + +poseidon.hash_many::( + &mut input_slice, + &mut output_slice, + test_size as u32, + rate as u32, + 8, // Output length + &config, +) +.unwrap(); +``` + +In the example above `Poseidon2::load(width, rate, MdsType::Default, DiffusionStrategy::Default, &ctx).unwrap();` is used to load the correct constants based on width and curve. Here, the default MDS matrices and diffusion are used. If you want to get a Plonky3 compliant version, set them to `MdsType::Plonky` and `DiffusionStrategy::Montgomery` respectively. + +## The Tree Builder + +Similar to Poseidon1, you can use Poseidon2 in a tree builder. + +```rust +use icicle_bn254::tree::Bn254TreeBuilder; +use icicle_bn254::poseidon2::Poseidon2; + +let mut config = TreeBuilderConfig::default(); +let arity = 2; +config.arity = arity as u32; +let input_block_len = arity; +let leaves = vec![F::one(); (1 << height) * arity]; +let mut digests = vec![F::zero(); merkle_tree_digests_len((height + 1) as u32, arity as u32, 1)]; + +let leaves_slice = HostSlice::from_slice(&leaves); +let digests_slice = HostSlice::from_mut_slice(&mut digests); + +let ctx = device_context::DeviceContext::default(); +let hash = Poseidon2::load(arity, arity, MdsType::Default, DiffusionStrategy::Default, &ctx).unwrap(); + +let mut config = TreeBuilderConfig::default(); +config.keep_rows = 5; +Bn254TreeBuilder::build_merkle_tree( + leaves_slice, + digests_slice, + height, + input_block_len, + &hash, + &hash, + &config, +) +.unwrap(); +``` diff --git a/docs/versioned_docs/version-3.0.0/icicle/primitives/vec_ops.md b/docs/versioned_docs/version-3.0.0/icicle/primitives/vec_ops.md new file mode 100644 index 000000000..e9e10c1a9 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/primitives/vec_ops.md @@ -0,0 +1,187 @@ + +# Vector Operations API + +## Overview + +The Vector Operations API in Icicle provides a set of functions for performing element-wise and scalar-vector operations on vectors, matrix operations, and miscellaneous operations like bit-reversal and slicing. These operations can be performed on the host or device, with support for asynchronous execution. + +### VecOpsConfig + +The `VecOpsConfig` struct is a configuration object used to specify parameters for vector operations. + +#### Fields + +- **`stream: icicleStreamHandle`**: Specifies the CUDA stream for asynchronous execution. If `nullptr`, the default stream is used. +- **`is_a_on_device: bool`**: Indicates whether the first input vector (`a`) is already on the device. If `false`, the vector will be copied from the host to the device. +- **`is_b_on_device: bool`**: Indicates whether the second input vector (`b`) is already on the device. If `false`, the vector will be copied from the host to the device. This field is optional. +- **`is_result_on_device: bool`**: Indicates whether the result should be stored on the device. If `false`, the result will be transferred back to the host. +- **`is_async: bool`**: Specifies whether the vector operation should be performed asynchronously. When `true`, the operation will not block the CPU, allowing other operations to proceed concurrently. Asynchronous execution requires careful synchronization to ensure data integrity. +- **`ext: ConfigExtension*`**: Backend-specific extensions. + +#### Default Configuration + +```cpp +static VecOpsConfig default_vec_ops_config() { + VecOpsConfig config = { + nullptr, // stream + false, // is_a_on_device + false, // is_b_on_device + false, // is_result_on_device + false, // is_async + }; + return config; +} +``` + +### Element-wise Operations + +These functions perform element-wise operations on two input vectors `a` and `b`, producing an output vector. + +#### `vector_add` + +Adds two vectors element-wise. + +```cpp +template +eIcicleError vector_add(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_sub` + +Subtracts vector `b` from vector `a` element-wise. + +```cpp +template +eIcicleError vector_sub(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_mul` + +Multiplies two vectors element-wise. + +```cpp +template +eIcicleError vector_mul(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_div` + +Divides vector `a` by vector `b` element-wise. + +```cpp +template +eIcicleError vector_div(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_accumulate` + +Adds vector b to a, inplace. + +```cpp +template +eIcicleError vector_accumulate(T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config); +``` + +#### `convert_montogomery` + +Convert a vector of field elements to/from montgomery form. +```cpp +template +eIcicleError convert_montgomery(const T* input, uint64_t size, bool is_into, const VecOpsConfig& config, T* output); +``` + +### Scalar-Vector Operations + +These functions apply a scalar operation to each element of a vector. + +#### `scalar_add_vec / scalar_sub_vec` + +Adds a scalar to each element of a vector. + +```cpp +template +eIcicleError scalar_add_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `scalar_sub_vec` + +Subtract each element of a vector from a scalar `scalar-vec`. + +```cpp +template +eIcicleError scalar_sub_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `scalar_mul_vec` + +Multiplies each element of a vector by a scalar. + +```cpp +template +eIcicleError scalar_mul_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +### Matrix Operations + +These functions perform operations on matrices. + +#### `matrix_transpose` + +Transposes a matrix. + +```cpp +template +eIcicleError matrix_transpose(const T* mat_in, uint32_t nof_rows, uint32_t nof_cols, const VecOpsConfig& config, T* mat_out); +``` + +### Miscellaneous Operations + +#### `bit_reverse` + +Reorders the vector elements based on a bit-reversal pattern. + +```cpp +template +eIcicleError bit_reverse(const T* vec_in, uint64_t size, const VecOpsConfig& config, T* vec_out); +``` + +#### `slice` + +Extracts a slice from a vector. + +```cpp +template +eIcicleError slice(const T* vec_in, uint64_t offset, uint64_t stride, uint64_t size, const VecOpsConfig& config, T* vec_out); +``` + +#### `highest_non_zero_idx` + +Finds the highest non-zero index in a vector. + +```cpp +template +eIcicleError highest_non_zero_idx(const T* vec_in, uint64_t size, const VecOpsConfig& config, int64_t* out_idx); +``` + +#### `polynomial_eval` + +Evaluates a polynomial at given domain points. + +```cpp +template +eIcicleError polynomial_eval(const T* coeffs, uint64_t coeffs_size, const T* domain, uint64_t domain_size, const VecOpsConfig& config, T* evals /*OUT*/); +``` + +#### `polynomial_division` + +Divides two polynomials. + +```cpp +template +eIcicleError polynomial_division(const T* numerator, int64_t numerator_deg, const T* denumerator, int64_t denumerator_deg, const VecOpsConfig& config, T* q_out /*OUT*/, uint64_t q_size, T* r_out /*OUT*/, uint64_t r_size); +``` + +### Rust and Go bindings + +- [Golang](../golang-bindings/vec-ops.md) +- [Rust](../rust-bindings/vec-ops.md) diff --git a/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/cpp.md b/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/cpp.md new file mode 100644 index 000000000..b8f1f285f --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/cpp.md @@ -0,0 +1,300 @@ +# Icicle C++ Usage Guide + +## Overview + +This guide covers the usage of ICICLE's C++ API, including device management, memory operations, data transfer, synchronization, and compute APIs. + +## Device Management + +:::note +See all ICICLE runtime APIs in [runtime.h](https://github.com/ingonyama-zk/icicle/blob/main/icicle/include/icicle/runtime.h) +::: + +### Loading a Backend + +The backend can be loaded from a specific path or from an environment variable. This is essential for setting up the computing environment. + +```cpp +#include "icicle/runtime.h" +eIcicleError result = icicle_load_backend_from_env_or_default(); +// or load from custom install dir +eIcicleError result = icicle_load_backend("/path/to/backend/installdir", true); +``` + +### Setting and Getting Active Device + +You can set the active device for the current thread and retrieve it when needed: + +```cpp +icicle::Device device = {"CUDA", 0}; // or other +eIcicleError result = icicle_set_device(device); +// or query current (thread) device +eIcicleError result = icicle_get_active_device(device); +``` + +### Querying Device Information + +Retrieve the number of available devices and check if a pointer is allocated on the host or on the active device: + +```cpp +int device_count; +eIcicleError result = icicle_get_device_count(device_count); + +bool is_host_memory; +eIcicleError result = icicle_is_host_memory(ptr); + +bool is_device_memory; +eIcicleError result = icicle_is_active_device_memory(ptr); +``` + +## Memory Management + +### Allocating and Freeing Memory + +Memory can be allocated and freed on the active device: + +```cpp +void* ptr; +eIcicleError result = icicle_malloc(&ptr, 1024); // Allocate 1024 bytes +eIcicleError result = icicle_free(ptr); // Free the allocated memory +``` + +### Asynchronous Memory Operations + +You can perform memory allocation and deallocation asynchronously using streams: + +```cpp +icicleStreamHandle stream; +eIcicleError err = icicle_create_stream(&stream); + +void* ptr; +err = icicle_malloc_async(&ptr, 1024, stream); +err = icicle_free_async(ptr, stream); +``` + +### Querying Available Memory + +Retrieve the total and available memory on the active device: + +```cpp +size_t total_memory, available_memory; +eIcicleError err = icicle_get_available_memory(total_memory, available_memory); +``` + +### Setting Memory Values + +Set memory to a specific value on the active device, synchronously or asynchronously: + +```cpp +eIcicleError err = icicle_memset(ptr, 0, 1024); // Set 1024 bytes to 0 +eIcicleError err = icicle_memset_async(ptr, 0, 1024, stream); +``` + +## Data Transfer + +### Copying Data + +Data can be copied between host and device, or between devices. The location of the memory is inferred from the pointers: + +```cpp +eIcicleError result = icicle_copy(dst, src, size); +eIcicleError result = icicle_copy_async(dst, src, size, stream); +``` + +### Explicit Data Transfers + +To avoid device-inference overhead, use explicit copy functions: + +```cpp +eIcicleError result = icicle_copy_to_host(host_dst, device_src, size); +eIcicleError result = icicle_copy_to_host_async(host_dst, device_src, size, stream); + +eIcicleError result = icicle_copy_to_device(device_dst, host_src, size); +eIcicleError result = icicle_copy_to_device_async(device_dst, host_src, size, stream); +``` + +## Stream Management + +### Creating and Destroying Streams + +Streams are used to manage asynchronous operations: + +```cpp +icicleStreamHandle stream; +eIcicleError result = icicle_create_stream(&stream); +eIcicleError result = icicle_destroy_stream(stream); +``` + +## Synchronization + +### Synchronizing Streams and Devices + +Ensure all previous operations on a stream or device are completed before proceeding: + +```cpp +eIcicleError result = icicle_stream_synchronize(stream); +eIcicleError result = icicle_device_synchronize(); +``` + +## Device Properties + +### Checking Device Availability + +Check if a device is available and retrieve a list of registered devices: + +```cpp +icicle::Device dev; +eIcicleError result = icicle_is_device_available(dev); +``` + +### Querying Device Properties + +Retrieve properties of the active device: + +```cpp +DeviceProperties properties; +eIcicleError result = icicle_get_device_properties(properties); + +/******************/ +// where DeviceProperties is +struct DeviceProperties { + bool using_host_memory; // Indicates if the device uses host memory + int num_memory_regions; // Number of memory regions available on the device + bool supports_pinned_memory; // Indicates if the device supports pinned memory + // Add more properties as needed +}; +``` + + +## Compute APIs + +### Multi-Scalar Multiplication (MSM) Example + +Icicle provides high-performance compute APIs such as the Multi-Scalar Multiplication (MSM) for cryptographic operations. Here's a simple example of how to use the MSM API. + +```cpp +#include +#include "icicle/runtime.h" +#include "icicle/api/bn254.h" + +using namespace bn254; + +int main() +{ + // Load installed backends + icicle_load_backend_from_env_or_default(); + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + const bool is_cuda_device_available = (eIcicleError::SUCCESS == icicle_is_device_available("CUDA")); + if (is_cuda_device_available) { + Device device = {"CUDA", 0}; // GPU-0 + ICICLE_CHECK(icicle_set_device(device)); // ICICLE_CHECK asserts that the api call returns eIcicleError::SUCCESS + } // else we stay on CPU backend + + // Setup inputs + int msm_size = 1024; + auto scalars = std::make_unique(msm_size); + auto points = std::make_unique(msm_size); + projective_t result; + + // Generate random inputs + scalar_t::rand_host_many(scalars.get(), msm_size); + projective_t::rand_host_many(points.get(), msm_size); + + // (optional) copy scalars to device memory explicitly + scalar_t* scalars_d = nullptr; + auto err = icicle_malloc((void**)&scalars_d, sizeof(scalar_t) * msm_size); + // Note: need to test err and make sure no errors occurred + err = icicle_copy(scalars_d, scalars.get(), sizeof(scalar_t) * msm_size); + + // MSM configuration + MSMConfig config = default_msm_config(); + // tell icicle that the scalars are on device. Note that EC points and result are on host memory in this example. + config.are_scalars_on_device = true; + + // Execute the MSM kernel (on the current device) + eIcicleError result_code = msm(scalars_d, points.get(), msm_size, config, &result); + // OR call bn254_msm(scalars_d, points.get(), msm_size, config, &result); + + // Free the device memory + icicle_free(scalars_d); + + // Check for errors + if (result_code == eIcicleError::SUCCESS) { + std::cout << "MSM result: " << projective_t::to_affine(result) << std::endl; + } else { + std::cerr << "MSM computation failed with error: " << get_error_string(result_code) << std::endl; + } + + return 0; +} +``` + +### Polynomial Operations Example + +Here's another example demonstrating polynomial operations using Icicle: + +```cpp +#include +#include "icicle/runtime.h" +#include "icicle/polynomials/polynomials.h" +#include "icicle/api/bn254.h" + +using namespace bn254; + +// define bn254Poly to be a polynomial over the scalar field of bn254 +using bn254Poly = Polynomial; + +static bn254Poly randomize_polynomial(uint32_t size) +{ + auto coeff = std::make_unique(size); + for (int i = 0; i < size; i++) + coeff[i] = scalar_t::rand_host(); + return bn254Poly::from_rou_evaluations(coeff.get(), size); +} + +int main() +{ + // Load backend and set device + icicle_load_backend_from_env_or_default(); + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + const bool is_cuda_device_available = (eIcicleError::SUCCESS == icicle_is_device_available("CUDA")); + if (is_cuda_device_available) { + Device device = {"CUDA", 0}; // GPU-0 + ICICLE_CHECK(icicle_set_device(device)); // ICICLE_CHECK asserts that the API call returns eIcicleError::SUCCESS + } // else we stay on CPU backend + + int poly_size = 1024; + + // build domain for ntt is required for some polynomial ops that rely on ntt + ntt_init_domain(scalar_t::omega(12), default_ntt_init_domain_config()); + + // randomize polynomials f(x),g(x) over the scalar field of bn254 + bn254Poly f = randomize_polynomial(poly_size); + bn254Poly g = randomize_polynomial(poly_size); + + // Perform polynomial multiplication + auto result = f * g; // Executes on the current device + + ICICLE_LOG_INFO << "Done"; + + return 0; +} +``` + +In this example, the polynomial multiplication is used to perform polynomial multiplication on CUDA or CPU, showcasing the flexibility and power of Icicle's compute APIs. + +## Error Handling + +### Checking for Errors + +Icicle APIs return an `eIcicleError` enumeration value. Always check the returned value to ensure that operations were successful. + +```cpp +if (result != eIcicleError::SUCCESS) { + // Handle error +} +``` + +This guide provides an overview of the essential APIs available in Icicle for C++. The provided examples should help you get started with integrating Icicle into your high-performance computing projects. diff --git a/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/general.md b/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/general.md new file mode 100644 index 000000000..b02cd2f9c --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/general.md @@ -0,0 +1,107 @@ + +# Icicle Programmer's Guide + +## Compute APIs + +ICICLE offers a variety of compute APIs, including Number Theoretic Transforms (NTT), Multi Scalar Multiplication (MSM), vector operations, Elliptic Curve NTT (ECNTT), polynomials, and more. These APIs follow a consistent structure, making it straightforward to apply the same usage patterns across different operations. + +[Check out all details about compute APIs here](../primitives/overview.md). + +### Common Structure of Compute APIs + +Each compute API in Icicle typically involves the following components: + +- **Inputs and Outputs**: The data to be processed and the resulting output are passed to the API functions. These can reside either on the host (CPU) or on a device (GPU). + +- **Parameters**: Parameters such as the size of data to be processed are provided to control the computation. + +- **Configuration Struct**: A configuration struct is used to specify additional options for the computation. This struct has default values but can be customized as needed. + +The configuration struct allows users to modify settings such as: + +- Specifying whether inputs and outputs are on the host or device. +- Adjusting the data layout for specific optimizations. +- Passing custom options to the backend implementation through an extension mechanism, such as setting the number of CPU cores to use. + +### Example (C++) + +```cpp +#include "icicle/vec_ops.h" + +// Create config struct for vector add +VecOpsConfig config = default_vec_ops_config(); +// optionally modify the config struct here + +// Call the API +eIcicleError err = vector_add(vec_a, vec_b, size, config, vec_res); +``` + +Where `VecOpsConfig` is defined in `icicle/vec_ops.h`: + +```cpp +struct VecOpsConfig { + icicleStreamHandle stream; /**< Stream for asynchronous execution. */ + bool is_a_on_device; /**< True if `a` is on the device, false if it is not. Default value: false. */ + bool is_b_on_device; /**< True if `b` is on the device, false if it is not. Default value: false. OPTIONAL. */ + bool is_result_on_device; /**< If true, the output is preserved on the device, otherwise on the host. Default value: false. */ + bool is_async; /**< Whether to run the vector operations asynchronously. */ + ConfigExtension* ext = nullptr; /**< Backend-specific extension. */ +}; +``` + +This pattern is consistent across most Icicle APIs, in C++/Rust/Go, providing flexibility while maintaining a familiar structure. For NTT, MSM, and other operations, include the corresponding header and call the template APIs. + +### Config struct extension + +In special cases, where an application wants to specify backend specific options, this is achieved with a config-extension struct. +For example the CPU backend has an option regarding how many threads to use for a vector addition looks as follows: +```cpp +#include "icicle/vec_ops.h" + +// Create config struct for vector add +VecOpsConfig config = default_vec_ops_config(); +ConfigExtension ext; +config.ext = &ext; +ext.set("n_threads", 8); // tell the CPU backend to use 8 threads +// Call the API +eIcicleError err = vector_add(vec_a, vec_b, size, config, vec_res); +``` + +:::note +This is not device-agnostic behavior, meaning such code is aware of the backend. +Having said that, it is not an error to pass options to a backend that is not aware of them. +::: + +## Device Abstraction + +ICICLE provides a device abstraction layer that allows you to interact with different compute devices such as CPUs and GPUs seamlessly. The device abstraction ensures that your code can work across multiple hardware platforms without modification. + +### Device Management + +- **Loading Backends**: Backends are loaded dynamically based on the environment configuration or a specified path. +- **Setting Active Device**: The active device for a thread can be set, allowing for targeted computation on a specific device. + +## Streams + +Streams in ICICLE allow for asynchronous execution and memory operations, enabling parallelism and non-blocking execution. Streams are associated with specific devices, and you can create, destroy, and synchronize streams to manage your workflow. + +:::note +For compute APIs, streams go into the `config.stream` field along with the `is_async=true` config flag. +::: + +### Memory Management + +Icicle provides functions for allocating, freeing, and managing memory across devices. Memory operations can be performed synchronously or asynchronously, depending on the use case. + +### Data Transfer + +Data transfer between the host and devices, or between different devices, is handled through a set of APIs that ensure efficient and error-checked operations. Asynchronous operations are supported to maximize performance. + +### Synchronization + +Synchronization ensures that all previous operations on a stream or device are completed. This is crucial when coordinating between multiple operations that depend on one another. + +## Additional Information + +- **Error Handling**: Icicle uses a specific error enumeration (`eIcicleError`) to handle and return error states across its APIs. +- **Device Properties**: You can query various properties of devices to tailor operations according to the hardware capabilities. diff --git a/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/go.md b/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/go.md new file mode 100644 index 000000000..d869c98f0 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/go.md @@ -0,0 +1,296 @@ +# ICICLE Golang Usage Guide + +## Overview + +This guide covers the usage of ICICLE's Golang API, including device management, memory operations, data transfer, synchronization, and compute APIs. + +## Device Management + +:::note +See all ICICLE runtime APIs in [runtime.go](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/golang/runtime/runtime.go) +::: + +### Loading a Backend + +The backend can be loaded from a specific path or from an environment variable. This is essential for setting up the computing environment. + +```go +import "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + +result := runtime.LoadBackendFromEnvOrDefault() +// or load from custom install dir +result := runtime.LoadBackend("/path/to/backend/installdir", true) +``` + +### Setting and Getting Active Device + +You can set the active device for the current thread and retrieve it when needed: + +```go +device = runtime.CreateDevice("CUDA", 0) // or other +result := runtime.SetDevice(device) +// or query current (thread) device +activeDevice := runtime.GetActiveDevice() +``` + +### Querying Device Information + +Retrieve the number of available devices and check if a pointer is allocated on the host or on the active device: + +```go +numDevices := runtime.GetDeviceCount() + +var ptr unsafe.Pointer +isHostMemory = runtime.IsHostMemory(ptr) +isDeviceMemory = runtime.IsActiveDeviceMemory(ptr) +``` + +## Memory Management + +### Allocating and Freeing Memory + +Memory can be allocated and freed on the active device: + +```go +ptr, err := runtime.Malloc(1024) // Allocate 1024 bytes +err := runtime.Free(ptr) // Free the allocated memory +``` + +### Asynchronous Memory Operations + +You can perform memory allocation and deallocation asynchronously using streams: + +```go +stream, err := runtime.CreateStream() + +ptr, err := runtime.MallocAsync(1024, stream) +err = runtime.FreeAsync(ptr, stream) +``` + +### Querying Available Memory + +Retrieve the total and available memory on the active device: + +```go +size_t total_memory, available_memory; +availableMemory, err := runtime.GetAvailableMemory() +freeMemory := availableMemory.Free +totalMemory := availableMemory.Total +``` + +### Setting Memory Values + +Set memory to a specific value on the active device, synchronously or asynchronously: + +```go +err := runtime.Memset(ptr, 0, 1024) // Set 1024 bytes to 0 +err := runtime.MemsetAsync(ptr, 0, 1024, stream) +``` + +## Data Transfer + +### Explicit Data Transfers + +To avoid device-inference overhead, use explicit copy functions: + +```go +result := runtime.CopyToHost(host_dst, device_src, size) +result := runtime.CopyToHostAsync(host_dst, device_src, size, stream) +result := runtime.CopyToDevice(device_dst, host_src, size) +result := runtime.CopyToDeviceAsync(device_dst, host_src, size, stream) +``` + +## Stream Management + +### Creating and Destroying Streams + +Streams are used to manage asynchronous operations: + +```go +stream, err := runtime.CreateStream() +err = runtime.DestroyStream(stream) +``` + +## Synchronization + +### Synchronizing Streams and Devices + +Ensure all previous operations on a stream or device are completed before proceeding: + +```go +err := runtime.StreamSynchronize(stream) +err := runtime.DeviceSynchronize() +``` + +## Device Properties + +### Checking Device Availability + +Check if a device is available and retrieve a list of registered devices: + +```go +dev := runtime.CreateDevice("CPU", 0) +isCPUAvail := runtime.IsDeviceAvailable(dev) +``` + +### Querying Device Properties + +Retrieve properties of the active device: + +```go +properties, err := runtime.GetDeviceProperties(properties); + +/******************/ +// where DeviceProperties is +type DeviceProperties struct { + UsingHostMemory bool // Indicates if the device uses host memory + NumMemoryRegions int32 // Number of memory regions available on the device + SupportsPinnedMemory bool // Indicates if the device supports pinned memory +} +``` + +## Compute APIs + +### Multi-Scalar Multiplication (MSM) Example + +Icicle provides high-performance compute APIs such as the Multi-Scalar Multiplication (MSM) for cryptographic operations. Here's a simple example of how to use the MSM API. + +```go +package main + +import ( + "fmt" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + bn254Msm "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" +) + +func main() { + + // Load installed backends + runtime.LoadBackendFromEnvOrDefault() + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + deviceCuda := runtime.CreateDevice("CUDA", 0) // GPU-0 + if runtime.IsDeviceAvailable(&deviceCuda) { + runtime.SetDevice(&deviceCuda) + } // else we stay on CPU backend + + // Setup inputs + const size = 1 << 18 + + // Generate random inputs + scalars := bn254.GenerateScalars(size) + points := bn254.GenerateAffinePoints(size) + + // (optional) copy scalars to device memory explicitly + var scalarsDevice core.DeviceSlice + scalars.CopyToDevice(&scalarsDevice, true) + + // MSM configuration + cfgBn254 := core.GetDefaultMSMConfig() + + // allocate memory for the result + result := make(core.HostSlice[bn254.Projective], 1) + + // execute bn254 MSM on device + err := bn254Msm.Msm(scalarsDevice, points, &cfgBn254, result) + + // Check for errors + if err != runtime.Success { + errorString := fmt.Sprint( + "bn254 Msm failed: ", err) + panic(errorString) + } + + // free explicitly allocated device memory + scalarsDevice.Free() +} +``` + +### Polynomial Operations Example + +Here's another example demonstrating polynomial operations using Icicle: + +```go +package main + +import ( + "fmt" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear/ntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear/polynomial" +) + +func initBabybearDomain() runtime.EIcicleError { + cfgInitDomain := core.GetDefaultNTTInitDomainConfig() + rouIcicle := babybear.ScalarField{} + rouIcicle.FromUint32(1461624142) + return ntt.InitDomain(rouIcicle, cfgInitDomain) +} + +func init() { + // Load installed backends + runtime.LoadBackendFromEnvOrDefault() + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + deviceCuda := runtime.CreateDevice("CUDA", 0) // GPU-0 + if runtime.IsDeviceAvailable(&deviceCuda) { + runtime.SetDevice(&deviceCuda) + } // else we stay on CPU backend + + // build domain for ntt is required for some polynomial ops that rely on ntt + err := initBabybearDomain() + if err != runtime.Success { + errorString := fmt.Sprint( + "Babybear Domain initialization failed: ", err) + panic(errorString) + } +} + +func main() { + + // Setup inputs + const polySize = 1 << 10 + + // randomize two polynomials over babybear field + var fBabybear polynomial.DensePolynomial + defer fBabybear.Delete() + var gBabybear polynomial.DensePolynomial + defer gBabybear.Delete() + fBabybear.CreateFromCoeffecitients(babybear.GenerateScalars(polySize)) + gBabybear.CreateFromCoeffecitients(babybear.GenerateScalars(polySize / 2)) + + // Perform polynomial multiplication + rBabybear := fBabybear.Multiply(&gBabybear) // Executes on the current device + defer rBabybear.Delete() + rDegree := rBabybear.Degree() + + fmt.Println("f Degree: ", fBabybear.Degree()) + fmt.Println("g Degree: ", gBabybear.Degree()) + fmt.Println("r Degree: ", rDegree) +} +``` + +In this example, the polynomial multiplication is used to perform polynomial multiplication on CUDA or CPU, showcasing the flexibility and power of Icicle's compute APIs. + +## Error Handling + +### Checking for Errors + +Icicle APIs return an `EIcicleError` enumeration value. Always check the returned value to ensure that operations were successful. + +```go +if result != runtime.SUCCESS { + // Handle error +} +``` + +This guide provides an overview of the essential APIs available in Icicle for C++. The provided examples should help you get started with integrating Icicle into your high-performance computing projects. diff --git a/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/rust.md b/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/rust.md new file mode 100644 index 000000000..af5caa5a8 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/programmers_guide/rust.md @@ -0,0 +1,247 @@ + +# Icicle Rust Usage Guide + +## Overview + +This guide covers the usage of ICICLE’s Rust API, including device management, memory operations, data transfer, synchronization, and compute APIs. + +## Build the Rust Application and Execute + +To successfully build and execute the Rust application using ICICLE, you need to define the ICICLE dependencies in your Cargo.toml file: + +```bash +[dependencies] +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-babybear = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +# add other ICICLE crates here as needed +``` + +Once the dependencies are defined, you can build and run your application using the following command: +```bash +cargo run --release +``` + +This will compile your Rust application with optimizations and execute it. + +:::note +The icicle-runtime crate is used to load backends, select a device, and interact with the device in an abstract way when managing memory, streams, and other resources, as explained in this guide. +::: + +## Device Management + +### Loading a Backend + +The backend can be loaded from a specific path or from an environment variable. This is essential for setting up the computing environment. + +```rust +use icicle_runtime::runtime; + +runtime::load_backend_from_env_or_default().unwrap(); +// or load from custom install dir +runtime::load_backend("/path/to/backend/installdir").unwrap(); +``` + +### Setting and Getting Active Device + +You can set the active device for the current thread and retrieve it when needed: + +```rust +use icicle_runtime::Device; + +let device = Device::new("CUDA", 0); // or other +icicle_runtime::set_device(&device).unwrap(); + +let active_device = icicle_runtime::get_active_device().unwrap(); +``` + +### Querying Device Information + +Retrieve the number of available devices and check if a pointer is allocated on the host or on the active device: + +```rust +let device_count = icicle_runtime::get_device_count().unwrap(); +``` + +## Memory Management + +### Allocating and Freeing Memory + +Memory can be allocated on the active device using the `DeviceVec` API. This memory allocation is flexible, as it supports allocation on any device, including the CPU if the CPU backend is used. + +```rust +use icicle_runtime::memory::DeviceVec; + +// Allocate 1024 elements on the device +let mut device_memory: DeviceVec = DeviceVec::::device_malloc(1024).unwrap(); +``` + +The memory is released when the `DeviceVec` object is dropped. + +### Asynchronous Memory Operations + +Asynchronous memory operations can be performed using streams. This allows for non-blocking execution, with memory allocation and deallocation occurring asynchronously. +```rust +use icicle_runtime::stream::IcicleStream; +use icicle_runtime::memory::DeviceVec; + +let mut stream = IcicleStream::create().unwrap(); // mutability is for the destroy() method + +// Allocate 1024 elements asynchronously on the device +let mut device_memory: DeviceVec = DeviceVec::::device_malloc_async(1024, &stream).unwrap(); + +// dispatch additional copy, compute etc. ops to the stream + +// Synchronize the stream to ensure all operations are complete +stream.synchronize().unwrap(); +stream.destroy().unwrap(); // +``` + +:::note +Streams need be explicitly destroyed before being dropped. +::: + +### Querying Available Memory + +You can retrieve the total and available memory on the active device using the `get_available_memory` function. + +```rust +use icicle_runtime::memory::get_available_memory; + +// Retrieve total and available memory on the active device +let (total_memory, available_memory) = get_available_memory().unwrap(); + +println!("Total memory: {}", total_memory); +println!("Available memory: {}", available_memory); +``` + +This function returns a tuple containing the total memory and the currently available memory on the device. It is essential for managing and optimizing resource usage in your applications. + +## Data Transfer + +### Copying Data + +Data can be copied between the host and device, or between devices. The location of the memory is handled by the `HostOrDeviceSlice` and `DeviceSlice` traits: + +```rust +use icicle_runtime::memory::{DeviceVec, HostSlice}; + +// Copy data from host to device +let input = vec![1, 2, 3, 4]; +let mut d_mem = DeviceVec::::device_malloc(input.len()).unwrap(); +d_mem.copy_from_host(HostSlice::from_slice(&input)).unwrap(); +// OR +d_mem.copy_from_host_async(HostSlice::from_slice(&input, &stream)).unwrap(); + +// Copy data back from device to host +let mut output = vec![0; input.len()]; +d_mem.copy_to_host(HostSlice::from_mut_slice(&mut output)).unwrap(); +// OR +d_mem.copy_to_host_async(HostSlice::from_mut_slice(&mut output, &stream)).unwrap(); +``` +## Stream Management + +### Creating and Destroying Streams + +Streams in Icicle are used to manage asynchronous operations, ensuring that computations can run in parallel without blocking the CPU thread: + +```rust +use icicle_runtime::stream::IcicleStream; + +// Create a stream +let mut stream = IcicleStream::create().unwrap(); + +// Destroy the stream +stream.destroy().unwrap(); +``` + +## Synchronization + +### Synchronizing Streams and Devices + +Synchronization ensures that all previous operations on a stream or device are completed before moving on to the next task. This is crucial when coordinating between multiple dependent operations: + +```rust +use icicle_runtime::stream::IcicleStream; + +// Synchronize the stream +stream.synchronize().unwrap(); + +// Synchronize the device +icicle_runtime::device_synchronize().unwrap(); +``` +These functions ensure that your operations are properly ordered and completed before the program proceeds, which is critical in parallel computing environments. + +## Device Properties + +### Checking Device Availability + +Check if a specific device is available and retrieve a list of registered devices: +```rust +use icicle_runtime::Device; + +let cuda_device = Device::new("CUDA", 0); +if icicle_runtime::is_device_available(&cuda_device) { + println!("CUDA device is available."); +} else { + println!("CUDA device is not available."); +} + +let registered_devices = icicle_runtime::get_registered_devices().unwrap(); +println!("Registered devices: {:?}", registered_devices); +``` + +### Querying Device Properties + +Retrieve properties of the active device to understand its capabilities and configurations: + +```rust +use icicle_runtime::Device; + +let cuda_device = Device::new("CUDA", 0); +if icicle_runtime::is_device_available(&cuda_device) { + icicle_runtime::set_device(&cuda_device); + let device_props = icicle_runtime::get_device_properties().unwrap(); + println!("Device using host memory: {}", device_props.using_host_memory); +} +``` + +These functions allow you to query device capabilities and ensure that your application is running on the appropriate hardware. + +## Compute APIs + +### Multi-Scalar Multiplication (MSM) Example + +Icicle provides high-performance compute APIs such as Multi-Scalar Multiplication (MSM) for cryptographic operations. Here's a simple example of how to use the MSM API in Rust. + +```rust +// Using bls12-377 curve +use icicle_bls12_377::curve::{CurveCfg, G1Projective, ScalarCfg}; +use icicle_core::{curve::Curve, msm, msm::MSMConfig, traits::GenerateRandom}; +use icicle_runtime::{device::Device, memory::HostSlice}; + +fn main() { + // Load backend and set device + let _ = icicle_runtime::runtime::load_backend_from_env_or_default(); + let cuda_device = Device::new("CUDA", 0); + if icicle_runtime::is_device_available(&cuda_device) { + icicle_runtime::set_device(&cuda_device).unwrap(); + } + + let size = 1024; + + // Randomize inputs + let points = CurveCfg::generate_random_affine_points(size); + let scalars = ScalarCfg::generate_random(size); + + let mut msm_results = vec![G1Projective::zero(); 1]; + msm::msm( + HostSlice::from_slice(&scalars), + HostSlice::from_slice(&points), + &MSMConfig::default(), + HostSlice::from_mut_slice(&mut msm_results[..]), + ) + .unwrap(); + println!("MSM result = {:?}", msm_results); +} +``` diff --git a/docs/versioned_docs/version-3.0.0/icicle/rust-bindings.md b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings.md new file mode 100644 index 000000000..bbfb16d9c --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings.md @@ -0,0 +1,64 @@ +# Rust bindings + +Rust bindings allow you to use ICICLE as a rust library. + +`icicle-core` defines all interfaces, macros and common methods. + +`icicle-runtime` contains runtime APIs for memory management, stream management and more. + +`icicle-curves` / `icicle-fields` implement all interfaces and macros from icicle-core for each curve. For example icicle-bn254 implements curve bn254. Each curve has its own build script which will build the ICICLE libraries for that curve as part of the rust-toolchain build. + +## Using ICICLE Rust bindings in your project + +Simply add the following to your `Cargo.toml`. + +```toml +# GPU Icicle integration +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git" } +icicle-bn254 = { git = "https://github.com/ingonyama-zk/icicle.git" } +``` + +`icicle-bn254` being the curve you wish to use and `icicle-core` and `icicle-runtime` contain ICICLE utilities and CUDA wrappers. + +If you wish to point to a specific ICICLE branch add `branch = ""` or `tag = ""` to the ICICLE dependency. For a specific commit add `rev = ""`. + +When you build your project ICICLE will be built as part of the build command. + +## How do the rust bindings work? + +The rust bindings are rust crates that wrap the ICICLE Core libraries (C++). Each crate can wrap one or more ICICLE core libraries. They are built too when building the crate. + +:::note +Since ICICLE V3, core libraries are shared-libraries. This means that they must be installed in a directory that can be found by the linker. In addition, installing an application that depends on ICICLE must make sure to install ICICLE or have it installed on the target machine. +::: + +## Supported curves, fields and operations + +### Supported curves and operations + +| Operation\Curve | bn254 | bls12_377 | bls12_381 | bw6-761 | grumpkin | +| --------------- | :---: | :-------: | :-------: | :-----: | :------: | +| MSM | ✅ | ✅ | ✅ | ✅ | ✅ | +| G2 | ✅ | ✅ | ✅ | ✅ | ❌ | +| NTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| VecOps | ✅ | ✅ | ✅ | ✅ | ✅ | +| Polynomials | ✅ | ✅ | ✅ | ✅ | ❌ | +| Poseidon | ✅ | ✅ | ✅ | ✅ | ✅ | +| Merkle Tree | ✅ | ✅ | ✅ | ✅ | ✅ | + +### Supported fields and operations + +| Operation\Field | babybear | stark252 | +| --------------- | :------: | :------: | +| VecOps | ✅ | ✅ | +| Polynomials | ✅ | ✅ | +| NTT | ✅ | ✅ | +| Extension Field | ✅ | ❌ | + +### Supported hashes + +| Hash | Sizes | +| ------ | :------: | +| Keccak | 256, 512 | diff --git a/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/ecntt.md b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/ecntt.md new file mode 100644 index 000000000..426324a93 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/ecntt.md @@ -0,0 +1,25 @@ +# ECNTT + +## ECNTT Method + +The `ecntt` function computes the Elliptic Curve Number Theoretic Transform (EC-NTT) or its inverse on a batch of points of a curve. + +```rust +pub fn ecntt( + input: &(impl HostOrDeviceSlice> + ?Sized), + dir: NTTDir, + cfg: &NTTConfig, + output: &mut (impl HostOrDeviceSlice> + ?Sized), +) -> Result<(), eIcicleError> +``` + +## Parameters + +- **`input`**: The input data as a slice of `Projective`. This represents points on a specific elliptic curve `C`. +- **`dir`**: The direction of the NTT. It can be `NTTDir::kForward` for forward NTT or `NTTDir::kInverse` for inverse NTT. +- **`cfg`**: The NTT configuration object of type `NTTConfig`. This object specifies parameters for the NTT computation, such as the batch size and algorithm to use. +- **`output`**: The output buffer to write the results into. This should be a slice of `Projective` with the same size as the input. + +## Return Value + +- **`Result<(), eIcicleError>`**: This function returns an `eIcicleError` which is a wrapper type that indicates success or failure of the NTT computation. On success, it contains `Ok(())`. diff --git a/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/keccak.md b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/keccak.md new file mode 100644 index 000000000..637ad3e63 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/keccak.md @@ -0,0 +1,98 @@ +# Keccak + +TODO update for V3 + +## Keccak Example + +```rust +use icicle_cuda_runtime::memory::{DeviceVec, HostSlice}; +use icicle_hash::keccak::{keccak256, HashConfig}; +use rand::{self, Rng}; + +fn main() { + let mut rng = rand::thread_rng(); + let initial_data: Vec = (0..120).map(|_| rng.gen::()).collect(); + println!("initial data: {}", hex::encode(&initial_data)); + let input = HostSlice::::from_slice(initial_data.as_slice()); + let mut output = DeviceVec::::cuda_malloc(32).unwrap(); + + let mut config = HashConfig::default(); + keccak256(input, initial_data.len() as i32, 1, &mut output[..], &mut config).expect("Failed to execute keccak256 hashing"); + + let mut output_host = vec![0_u8; 32]; + output.copy_to_host(HostSlice::from_mut_slice(&mut output_host[..])).unwrap(); + + println!("keccak256 result: {}", hex::encode(&output_host)); +} +``` + +## Keccak Methods + +```rust +pub fn keccak256( + input: &(impl HostOrDeviceSlice + ?Sized), + input_block_size: i32, + number_of_blocks: i32, + output: &mut (impl HostOrDeviceSlice + ?Sized), + config: &mut HashConfig, +) -> IcicleResult<()> + +pub fn keccak512( + input: &(impl HostOrDeviceSlice + ?Sized), + input_block_size: i32, + number_of_blocks: i32, + output: &mut (impl HostOrDeviceSlice + ?Sized), + config: &mut HashConfig, +) -> IcicleResult<()> +``` + +### Parameters + +- **`input`**: A slice containing the input data for the Keccak256 hash function. It can reside in either host memory or device memory. +- **`input_block_size`**: An integer specifying the size of the input data for a single hash. +- **`number_of_blocks`**: An integer specifying the number of results in the hash batch. +- **`output`**: A slice where the resulting hash will be stored. This slice can be in host or device memory. +- **`config`**: A pointer to a `HashConfig` object, which contains various configuration options for the Keccak256 operation. + +### Return Value + +- **`IcicleResult`**: Returns a CUDA error code indicating the success or failure of the Keccak256/Keccak512 operation. + +## HashConfig + +The `HashConfig` structure holds configuration parameters for the Keccak256/Keccak512 operation, allowing customization of its behavior to optimize performance based on the specifics of the operation or the underlying hardware. + +```rust +pub struct HashConfig<'a> { + pub ctx: DeviceContext<'a>, + pub are_inputs_on_device: bool, + pub are_outputs_on_device: bool, + pub is_async: bool, +} +``` + +### Fields + +- **`ctx`**: Device context containing details like device id and stream. +- **`are_inputs_on_device`**: Indicates if input data is located on the device. +- **`are_outputs_on_device`**: Indicates if output hash is stored on the device. +- **`is_async`**: If true, runs the Keccak256/Keccak512 operation asynchronously. + +### Usage + +Example initialization with default settings: + +```rust +let default_config = HashConfig::default(); +``` + +Customizing the configuration: + +```rust +let custom_config = NTTConfig { + ctx: custom_device_context, + are_inputs_on_device: true, + are_outputs_on_device: true, + is_async: false, +}; +``` \ No newline at end of file diff --git a/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/msm.md b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/msm.md new file mode 100644 index 000000000..e4f86444f --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/msm.md @@ -0,0 +1,117 @@ +# MSM + +## MSM API Overview + +```rust +pub fn msm>( + scalars: &(impl HostOrDeviceSlice + ?Sized), + bases: &(impl HostOrDeviceSlice> + ?Sized), + cfg: &MSMConfig, + results: &mut (impl HostOrDeviceSlice> + ?Sized), +) -> Result<(), eIcicleError>; +``` + +### Parameters + +- **`scalars`**: A buffer containing the scalar values to be multiplied with corresponding points. +- **`points`**: A buffer containing the points to be multiplied by the scalars. +- **`cfg`**: MSM configuration specifying additional parameters for the operation. +- **`results`**: A buffer where the results of the MSM operations will be stored. + +### MSM Config + +```rust +pub struct MSMConfig { + pub stream_handle: IcicleStreamHandle, + pub precompute_factor: i32, + pub c: i32, + pub bitsize: i32, + batch_size: i32, + are_points_shared_in_batch: bool, + are_scalars_on_device: bool, + pub are_scalars_montgomery_form: bool, + are_points_on_device: bool, + pub are_points_montgomery_form: bool, + are_results_on_device: bool, + pub is_async: bool, + pub ext: ConfigExtension, +} +``` + +- **`stream_handle: IcicleStreamHandle`**: Specifies a stream for asynchronous execution. +- **`precompute_factor: i32`**: Determines the number of extra points to pre-compute for each point, affecting memory footprint and performance. +- **`c: i32`**: The "window bitsize," a parameter controlling the computational complexity and memory footprint of the MSM operation. +- **`bitsize: i32`**: The number of bits of the largest scalar, typically equal to the bit size of the scalar field. +- **`batch_size: i32`**: The number of MSMs to compute in a single batch, for leveraging parallelism. +- **`are_scalars_montgomery_form`**: Set to `true` if scalars are in montgomery form. +- **`are_points_montgomery_form`**: Set to `true` if points are in montgomery form. +- **`are_scalars_on_device: bool`**, **`are_points_on_device: bool`**, **`are_results_on_device: bool`**: Indicate whether the corresponding buffers are on the device memory. +- **`is_async: bool`**: Whether to perform the MSM operation asynchronously. +- **`ext: ConfigExtension`**: extended configuration for backend. + +### Usage + +The `msm` function is designed to compute the sum of multiple scalar-point multiplications efficiently. It supports both single MSM operations and batched operations for increased performance. The configuration allows for detailed control over the execution environment and performance characteristics of the MSM operation. + +When performing MSM operations, it's crucial to match the size of the `scalars` and `points` arrays correctly and ensure that the `results` buffer is appropriately sized to hold the output. The `MSMConfig` should be set up to reflect the specifics of the operation, including whether the operation should be asynchronous and any device-specific settings. + +## Example + +```rust +// Using bls12-377 curve +use icicle_bls12_377::curve::{CurveCfg, G1Projective, ScalarCfg}; +use icicle_core::{curve::Curve, msm, msm::MSMConfig, traits::GenerateRandom}; +use icicle_runtime::{device::Device, memory::HostSlice}; + +fn main() { + // Load backend and set device ... + + // Randomize inputs + let size = 1024; + let points = CurveCfg::generate_random_affine_points(size); + let scalars = ScalarCfg::generate_random(size); + + let mut msm_results = vec![G1Projective::zero(); 1]; + msm::msm( + HostSlice::from_slice(&scalars), + HostSlice::from_slice(&points), + &MSMConfig::default(), + HostSlice::from_mut_slice(&mut msm_results[..]), + ) + .unwrap(); + println!("MSM result = {:?}", msm_results); +} + +``` + +## Batched msm + +For batch msm, simply allocate the results array with size corresponding to batch size and set the `are_points_shared_in_batch` flag in config struct. + +## Precomputationg + +Precomputes bases for the multi-scalar multiplication (MSM) by extending each base point with its multiples, facilitating more efficient MSM calculations. + +```rust +/// Returns `Ok(())` if no errors occurred or a `eIcicleError` otherwise. +pub fn precompute_bases>( + points: &(impl HostOrDeviceSlice> + ?Sized), + config: &MSMConfig, + output_bases: &mut DeviceSlice>, +) -> Result<(), eIcicleError>; +``` + +### Parameters + +- **`points`**: The original set of affine points (\(P_1, P_2, ..., P_n\)) to be used in the MSM. For batch MSM operations, this should include all unique points concatenated together. +- **`msm_size`**: The size of a single msm in order to determine optimal parameters. +- **`cfg`**: The MSM configuration parameters. +- **`output_bases`**: The output buffer for the extended bases. Its size must be `points.len() * precompute_factor`. This buffer should be allocated on the device for GPU computations. + +#### Returns + +`Ok(())` if the operation is successful, or an `eIcicleError` error otherwise. + +## Parameters for optimal performance + +Please refer to the [primitive description](../primitives/msm#choosing-optimal-parameters) diff --git a/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/multi-gpu.md b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/multi-gpu.md new file mode 100644 index 000000000..527e780ee --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/multi-gpu.md @@ -0,0 +1,204 @@ +# Multi GPU APIs + +TODO update for V3 + +To learn more about the theory of Multi GPU programming refer to [this part](../multi-device.md) of documentation. + +Here we will cover the core multi GPU apis and a [example](#a-multi-gpu-example) + + +## A Multi GPU example + +In this example we will display how you can + +1. Fetch the number of devices installed on a machine +2. For every GPU launch a thread and set an active device per thread. +3. Execute a MSM on each GPU + + + +```rust + +... + +let device_count = get_device_count().unwrap(); + +(0..device_count) + .into_par_iter() + .for_each(move |device_id| { + set_device(device_id).unwrap(); + + // you can allocate points and scalars_d here + + let mut cfg = MSMConfig::default_for_device(device_id); + cfg.ctx.stream = &stream; + cfg.is_async = true; + cfg.are_scalars_montgomery_form = true; + msm(&scalars_d, &HostOrDeviceSlice::on_host(points), &cfg, &mut msm_results).unwrap(); + + // collect and process results + }) + +... +``` + + +We use `get_device_count` to fetch the number of connected devices, device IDs will be `0, 1, 2, ..., device_count - 1` + +[`into_par_iter`](https://docs.rs/rayon/latest/rayon/iter/trait.IntoParallelIterator.html#tymethod.into_par_iter) is a parallel iterator, you should expect it to launch a thread for every iteration. + +We then call `set_device(device_id).unwrap();` it should set the context of that thread to the selected `device_id`. + +Any data you now allocate from the context of this thread will be linked to the `device_id`. We create our `MSMConfig` with the selected device ID `let mut cfg = MSMConfig::default_for_device(device_id);`, behind the scene this will create for us a `DeviceContext` configured for that specific GPU. + +We finally call our `msm` method. + + +## Device management API + +To streamline device management we offer as part of `icicle-cuda-runtime` package methods for dealing with devices. + +#### [`set_device`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L6) + +Sets the current CUDA device by its ID, when calling `set_device` it will set the current thread to a CUDA device. + +**Parameters:** + +- **`device_id: usize`**: The ID of the device to set as the current device. Device IDs start from 0. + +**Returns:** + +- **`CudaResult<()>`**: An empty result indicating success if the device is set successfully. In case of failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if the specified device ID is invalid or if a CUDA-related error occurs during the operation. + +**Example:** + +```rust +let device_id = 0; // Device ID to set +match set_device(device_id) { + Ok(()) => println!("Device set successfully."), + Err(e) => eprintln!("Failed to set device: {:?}", e), +} +``` + +#### [`get_device_count`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L10) + +Retrieves the number of CUDA devices available on the machine. + +**Returns:** + +- **`CudaResult`**: The number of available CUDA devices. On success, contains the count of CUDA devices. On failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if a CUDA-related error occurs during the retrieval of the device count. + +**Example:** + +```rust +match get_device_count() { + Ok(count) => println!("Number of devices available: {}", count), + Err(e) => eprintln!("Failed to get device count: {:?}", e), +} +``` + +#### [`get_device`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L15) + +Retrieves the ID of the current CUDA device. + +**Returns:** + +- **`CudaResult`**: The ID of the current CUDA device. On success, contains the device ID. On failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if a CUDA-related error occurs during the retrieval of the current device ID. + +**Example:** + +```rust +match get_device() { + Ok(device_id) => println!("Current device ID: {}", device_id), + Err(e) => eprintln!("Failed to get current device: {:?}", e), +} +``` + +## Device context API + +The `DeviceContext` is embedded into `NTTConfig`, `MSMConfig` and `PoseidonConfig`, meaning you can simply pass a `device_id` to your existing config and the same computation will be triggered on a different device. + +#### [`DeviceContext`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L11) + +Represents the configuration a CUDA device, encapsulating the device's stream, ID, and memory pool. The default device is always `0`. + +```rust +pub struct DeviceContext<'a> { + pub stream: &'a CudaStream, + pub device_id: usize, + pub mempool: CudaMemPool, +} +``` + +##### Fields + +- **`stream: &'a CudaStream`** + + A reference to a `CudaStream`. This stream is used for executing CUDA operations. By default, it points to a null stream CUDA's default execution stream. + +- **`device_id: usize`** + + The index of the GPU currently in use. The default value is `0`, indicating the first GPU in the system. + + In some cases assuming `CUDA_VISIBLE_DEVICES` was configured, for example as `CUDA_VISIBLE_DEVICES=2,3,7` in the system with 8 GPUs - the `device_id=0` will correspond to GPU with id 2. So the mapping may not always be a direct reflection of the number of GPUs installed on a system. + +- **`mempool: CudaMemPool`** + + Represents the memory pool used for CUDA memory allocations. The default is set to a null pointer, which signifies the use of the default CUDA memory pool. + +##### Implementation Notes + +- The `DeviceContext` structure is cloneable and can be debugged, facilitating easier logging and duplication of contexts when needed. + + +#### [`DeviceContext::default_for_device(device_id: usize) -> DeviceContext<'static>`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L30) + +Provides a default `DeviceContext` with system-wide defaults, ideal for straightforward setups. + +#### Returns + +A `DeviceContext` instance configured with: +- The default stream (`null_mut()`). +- The default device ID (`0`). +- The default memory pool (`null_mut()`). + +#### Parameters + +- **`device_id: usize`**: The ID of the device for which to create the context. + +#### Returns + +A `DeviceContext` instance with the provided `device_id` and default settings for the stream and memory pool. + + +#### [`check_device(device_id: i32)`](https://github.com/vhnatyk/icicle/blob/eef6876b037a6b0797464e7cdcf9c1ecfcf41808/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L42) + +Validates that the specified `device_id` matches the ID of the currently active device, ensuring operations are targeted correctly. + +#### Parameters + +- **`device_id: i32`**: The device ID to verify against the currently active device. + +#### Behavior + +- **`Panics`** if the `device_id` does not match the active device's ID, preventing cross-device operation errors. + +#### Example + +```rust +let device_id: i32 = 0; // Example device ID +check_device(device_id); +// Ensures that the current context is correctly set for the specified device ID. +``` diff --git a/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/ntt.md b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/ntt.md new file mode 100644 index 000000000..06758972e --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/ntt.md @@ -0,0 +1,106 @@ +# NTT + +## NTT API overview + +```rust +pub fn ntt( + input: &(impl HostOrDeviceSlice + ?Sized), + dir: NTTDir, + cfg: &NTTConfig, + output: &mut (impl HostOrDeviceSlice + ?Sized), +) -> Result<(), eIcicleError>; + +pub fn ntt_inplace( + inout: &mut (impl HostOrDeviceSlice + ?Sized), + dir: NTTDir, + cfg: &NTTConfig, +) -> Result<(), eIcicleError> +``` + +- **`input`** - buffer to read the inputs of the NTT from. +- **`dir`** - whether to compute forward or inverse NTT. +- **`cfg`** - config used to specify extra arguments of the NTT. +- **`output`** - buffer to write the NTT outputs into. Must be of the same size as input. + +The `input` and `output` buffers can be on device or on host. Being on host means that they will be transferred to device during runtime. + +### NTT Config + +```rust +pub struct NTTConfig { + pub stream_handle: IcicleStreamHandle, + pub coset_gen: S, + pub batch_size: i32, + pub columns_batch: bool, + pub ordering: Ordering, + pub are_inputs_on_device: bool, + pub are_outputs_on_device: bool, + pub is_async: bool, + pub ext: ConfigExtension, +} +``` + +The `NTTConfig` struct is a configuration object used to specify parameters for an NTT instance. + +#### Fields + +- **`stream_handle: IcicleStreamHandle`**: Specifies the stream (queue) to use for async execution + +- **`coset_gen: S`**: Defines the coset generator used for coset (i)NTTs. By default, this is set to `S::one()`, indicating that no coset is being used. + +- **`batch_size: i32`**: Determines the number of NTTs to compute in a single batch. The default value is 1, meaning that operations are performed on individual inputs without batching. Batch processing can significantly improve performance by leveraging parallelism in GPU computations. + +- **`columns_batch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. Defaults to `false`. + +- **`ordering: Ordering`**: Controls the ordering of inputs and outputs for the NTT operation. This field can be used to specify decimation strategies (in time or in frequency) and the type of butterfly algorithm (Cooley-Tukey or Gentleman-Sande). The ordering is crucial for compatibility with various algorithmic approaches and can impact the efficiency of the NTT. + +- **`are_inputs_on_device: bool`**: Indicates whether the input data has been preloaded on the device memory. If `false` inputs will be copied from host to device. + +- **`are_outputs_on_device: bool`**: Indicates whether the output data is preloaded in device memory. If `false` outputs will be copied from host to device. If the inputs and outputs are the same pointer NTT will be computed in place. + +- **`is_async: bool`**: Specifies whether the NTT operation should be performed asynchronously. When set to `true`, the NTT function will not block the CPU, allowing other operations to proceed concurrently. Asynchronous execution requires careful synchronization to ensure data integrity and correctness. +- **`ext: ConfigExtension`**: extended configuration for backend. + +#### Example + +```rust +// Setting Bn254 points and scalars +println!("Generating random inputs on host for bn254..."); +let scalars = Bn254ScalarCfg::generate_random(size); +let mut ntt_results = DeviceVec::::device_malloc(size).unwrap(); + +// constructing NTT domain +initialize_domain( + ntt::get_root_of_unity::( + size.try_into() + .unwrap(), + ), + &ntt::NTTInitDomainConfig::default(), +) +.unwrap(); + +// Using default config +let cfg = ntt::NTTConfig::::default(); + +// Computing NTT +ntt::ntt( + HostSlice::from_slice(&scalars), + ntt::NTTDir::kForward, + &cfg, + &mut ntt_results[..], +) +.unwrap(); +``` + +### NTT Domain + +Before performing NTT operations, it is mandatory to construct the domain as [explained here](../primitives/ntt.md#ntt-domain). +In rust, we have the following functions to construct, destruct the domain and retrieve a root of unity from it: + +```rust +pub trait NTTDomain { + pub fn initialize_domain(primitive_root: F, config: &NTTInitDomainConfig) -> Result<(), eIcicleError>; + pub fn release_domain() -> Result<(), eIcicleError>; + pub fn get_root_of_unity(max_size: u64) -> F; +} +``` diff --git a/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/polynomials.md b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/polynomials.md new file mode 100644 index 000000000..edb99c917 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/polynomials.md @@ -0,0 +1,284 @@ +# Rust FFI Bindings for Univariate Polynomial + +:::note +Please refer to the Polynomials overview page for a deep overview. This section is a brief description of the Rust FFI bindings. +::: + +This documentation is designed to provide developers with a clear understanding of how to utilize the Rust bindings for polynomial operations efficiently and effectively, leveraging the robust capabilities of both Rust and C++ in their applications. + +## Introduction + +The Rust FFI bindings for the Univariate Polynomial serve as a "shallow wrapper" around the underlying C++ implementation. These bindings provide a straightforward Rust interface that directly calls functions from a C++ library, effectively bridging Rust and C++ operations. The Rust layer handles simple interface translations without delving into complex logic or data structures, which are managed on the C++ side. This design ensures efficient data handling, memory management, and execution of polynomial operations directly via C++. +Currently, these bindings are tailored specifically for polynomials where the coefficients, domain, and images are represented as scalar fields. + +## Initialization Requirements + +Before utilizing any functions from the polynomial API, it is mandatory to initialize the appropriate polynomial backend (e.g., CUDA). Additionally, the NTT (Number Theoretic Transform) domain must also be initialized, as the CUDA backend relies on this for certain operations. Failing to properly initialize these components can result in errors. + +:::note +**Field-Specific Initialization Requirement** + +The ICICLE library is structured such that each field or curve has its dedicated library implementation. As a result, initialization must be performed individually for each field or curve to ensure the correct setup and functionality of the library. +::: + +## Core Trait: `UnivariatePolynomial` + +The `UnivariatePolynomial` trait encapsulates the essential functionalities required for managing univariate polynomials in the Rust ecosystem. This trait standardizes the operations that can be performed on polynomials, regardless of the underlying implementation details. It allows for a unified approach to polynomial manipulation, providing a suite of methods that are fundamental to polynomial arithmetic. + +### Trait Definition + +```rust +pub trait UnivariatePolynomial +where + Self::Field: FieldImpl, + Self::FieldConfig: FieldConfig, +{ + type Field: FieldImpl; + type FieldConfig: FieldConfig; + + // Methods to create polynomials from coefficients or roots-of-unity evaluations. + fn from_coeffs + ?Sized>(coeffs: &S, size: usize) -> Self; + fn from_rou_evals + ?Sized>(evals: &S, size: usize) -> Self; + + // Method to divide this polynomial by another, returning quotient and remainder. + fn divide(&self, denominator: &Self) -> (Self, Self) where Self: Sized; + + // Method to divide this polynomial by the vanishing polynomial 'X^N-1'. + fn div_by_vanishing(&self, degree: u64) -> Self; + + // Methods to add or subtract a monomial in-place. + fn add_monomial_inplace(&mut self, monomial_coeff: &Self::Field, monomial: u64); + fn sub_monomial_inplace(&mut self, monomial_coeff: &Self::Field, monomial: u64); + + // Method to slice the polynomial, creating a sub-polynomial. + fn slice(&self, offset: u64, stride: u64, size: u64) -> Self; + + // Methods to return new polynomials containing only the even or odd terms. + fn even(&self) -> Self; + fn odd(&self) -> Self; + + // Method to evaluate the polynomial at a given domain point. + fn eval(&self, x: &Self::Field) -> Self::Field; + + // Method to evaluate the polynomial over a domain and store the results. + fn eval_on_domain + ?Sized, E: HostOrDeviceSlice + ?Sized>( + &self, + domain: &D, + evals: &mut E, + ); + + // Method to evaluate the polynomial over the roots-of-unity domain for power-of-two sized domain + fn eval_on_rou_domain + ?Sized>(&self, domain_log_size: u64, evals: &mut E); + + // Method to retrieve a coefficient at a specific index. + fn get_coeff(&self, idx: u64) -> Self::Field; + + // Method to copy coefficients into a provided slice. + fn copy_coeffs + ?Sized>(&self, start_idx: u64, coeffs: &mut S); + + // Method to get the degree of the polynomial. + fn degree(&self) -> i64; +} +``` + +## `DensePolynomial` Struct + +The DensePolynomial struct represents a dense univariate polynomial in Rust, leveraging a handle to manage its underlying memory within the CUDA device context. This struct acts as a high-level abstraction over complex C++ memory management practices, facilitating the integration of high-performance polynomial operations through Rust's Foreign Function Interface (FFI) bindings. + +```rust +pub struct DensePolynomial { + handle: PolynomialHandle, +} +``` + +### Traits implementation and methods + +#### `Drop` + +Ensures proper resource management by releasing the CUDA memory when a DensePolynomial instance goes out of scope. This prevents memory leaks and ensures that resources are cleaned up correctly, adhering to Rust's RAII (Resource Acquisition Is Initialization) principles. + +#### `Clone` + +Provides a way to create a new instance of a DensePolynomial with its own unique handle, thus duplicating the polynomial data in the CUDA context. Cloning is essential since the DensePolynomial manages external resources, which cannot be safely shared across instances without explicit duplication. + +#### Operator Overloading: `Add`, `Sub`, `Mul`, `Rem`, `Div` + +These traits are implemented for references to DensePolynomial (i.e., &DensePolynomial), enabling natural mathematical operations such as addition (+), subtraction (-), multiplication (*), division (/), and remainder (%). This syntactic convenience allows users to compose complex polynomial expressions in a way that is both readable and expressive. + +#### Key Methods + +In addition to the traits, the following methods are implemented: + +```rust +impl DensePolynomial { + // Returns a mutable slice of the polynomial coefficients on the device + pub fn coeffs_mut_slice(&mut self) -> &mut DeviceSlice {...} +} +``` + +## Flexible Memory Handling With `HostOrDeviceSlice` + +The DensePolynomial API is designed to accommodate a wide range of computational environments by supporting both host and device memory through the `HostOrDeviceSlice` trait. This approach ensures that polynomial operations can be seamlessly executed regardless of where the data resides, making the API highly adaptable and efficient for various hardware configurations. + +### Overview of `HostOrDeviceSlice` + +The HostOrDeviceSlice is a Rust trait that abstracts over slices of memory that can either be on the host (CPU) or the device (GPU), as managed by CUDA. This abstraction is crucial for high-performance computing scenarios where data might need to be moved between different memory spaces depending on the operations being performed and the specific hardware capabilities available. + +### Usage in API Functions + +Functions within the DensePolynomial API that deal with polynomial coefficients or evaluations use the HostOrDeviceSlice trait to accept inputs. This design allows the functions to be agnostic of the actual memory location of the data, whether it's in standard system RAM accessible by the CPU or in GPU memory accessible by CUDA cores. + +```rust +// Assume `coeffs` could either be in host memory or CUDA device memory +let coeffs: DeviceSlice = DeviceVec::::device_malloc(coeffs_len).unwrap(); +let p_from_coeffs = PolynomialBabyBear::from_coeffs(&coeffs, coeffs.len()); + +// Similarly for evaluations from roots of unity +let evals: HostSlice = HostSlice::from_slice(&host_memory_evals); +let p_from_evals = PolynomialBabyBear::from_rou_evals(&evals, evals.len()); + +// Same applies for any API that accepts HostOrDeviceSlice +``` + +## Usage + +This section outlines practical examples demonstrating how to utilize the `DensePolynomial` Rust API. The API is flexible, supporting multiple scalar fields. Below are examples showing how to use polynomials defined over different fields and perform a variety of operations. + +### Initialization and Basic Operations + +First, choose the appropriate field implementation for your polynomial operations, initializing the CUDA backend if necessary + +```rust +use icicle_babybear::polynomials::DensePolynomial as PolynomialBabyBear; + +let f = PolynomialBabyBear::from_coeffs(...); + +// now use f by calling the implemented traits + +// For operations over another field, such as BN254 +use icicle_bn254::polynomials::DensePolynomial as PolynomialBn254; +// Use PolynomialBn254 similarly +``` + +### Creation + +Polynomials can be created from coefficients or evaluations: + +```rust +let coeffs = ...; +let p_from_coeffs = PolynomialBabyBear::from_coeffs(HostSlice::from_slice(&coeffs), size); + +let evals = ...; +let p_from_evals = PolynomialBabyBear::from_rou_evals(HostSlice::from_slice(&evals), size); + +``` + +### Arithmetic Operations + +Utilize overloaded operators for intuitive mathematical expressions: + +```rust +let add = &f + &g; // Addition +let sub = &f - &g; // Subtraction +let mul = &f * &g; // Multiplication +let mul_scalar = &f * &scalar; // Scalar multiplication +``` + +### Division and Remainder + +Compute quotient and remainder or perform division by a vanishing polynomial: + +```rust +let (q, r) = f.divide(&g); // Compute both quotient and remainder +let q = &f / &g; // Quotient +let r = &f % &g; // Remainder + +let h = f.div_by_vanishing(N); // Division by V(x) = X^N - 1 + +``` + +### Monomial Operations + +Add or subtract monomials in-place for efficient polynomial manipulation: + +```rust +f.add_monomial_inplace(&three, 1 /*monmoial*/); // Adds 3*x to f +f.sub_monomial_inplace(&one, 0 /*monmoial*/); // Subtracts 1 from f +``` + +### Slicing + +Extract specific components: + +```rust +let even = f.even(); // Polynomial of even-indexed terms +let odd = f.odd(); // Polynomial of odd-indexed terms +let arbitrary_slice = f.slice(offset, stride, size); +``` + +### Evaluate + +Evaluate the polynoomial: + +```rust +let x = rand(); // Random field element +let f_x = f.eval(&x); // Evaluate f at x + +// Evaluate on a predefined domain +let domain = [one, two, three]; +let mut host_evals = vec![ScalarField::zero(); domain.len()]; +f.eval_on_domain(HostSlice::from_slice(&domain), HostSlice::from_mut_slice(&mut host_evals)); + +// Evaluate on roots-of-unity-domain +let domain_log_size = 4; +let mut device_evals = DeviceVec::::device_malloc(1 << domain_log_size).unwrap(); +f.eval_on_rou_domain(domain_log_size, &mut device_evals[..]); +``` + +### Read coefficients + +Read or copy polynomial coefficients for further processing: + +```rust +let x_squared_coeff = f.get_coeff(2); // Coefficient of x^2 + +// Copy coefficients to a device-specific memory space +let mut device_mem = DeviceVec::::device_malloc(coeffs.len()).unwrap(); +f.copy_coeffs(0, &mut device_mem[..]); +``` + +### Polynomial Degree + +Determine the highest power of the variable with a non-zero coefficient: + +```rust +let deg = f.degree(); // Degree of the polynomial +``` + +### Memory Management: Views (rust slices) + +Rust enforces correct usage of views at compile time, eliminating the need for runtime checks: + +```rust +let mut f = Poly::from_coeffs(HostSlice::from_slice(&coeffs), size); + +// Obtain a mutable slice of coefficients as a DeviceSlice +let coeffs_slice_dev = f.coeffs_mut_slice(); + +// Operations on f are restricted here due to mutable borrow of coeffs_slice_dev + +// Compute evaluations or perform other operations directly using the slice +// example: evaluate f on a coset of roots-of-unity. Computing from GPU to HOST/GPU +let mut config: NTTConfig<'_, F> = NTTConfig::default(); +config.coset_gen = /*some coset gen*/; +let mut coset_evals = vec![F::zero(); coeffs_slice_dev.len()]; +ntt( + coeffs_slice_dev, + NTTDir::kForward, + &config, + HostSlice::from_mut_slice(&mut coset_evals), +) +.unwrap(); + +// now can f can be borrowed once again +``` diff --git a/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/vec-ops.md b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/vec-ops.md new file mode 100644 index 000000000..61aa71570 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/icicle/rust-bindings/vec-ops.md @@ -0,0 +1,104 @@ +# Vector Operations API + +Our vector operations API includes fundamental methods for addition, subtraction, and multiplication of vectors, with support for both host and device memory. + +## Vector Operations Configuration + +The `VecOpsConfig` struct encapsulates the settings for vector operations, including device context and operation modes. + +### `VecOpsConfig` + +Defines configuration parameters for vector operations. + +```rust +pub struct VecOpsConfig { + pub stream_handle: IcicleStreamHandle, + pub is_a_on_device: bool, + pub is_b_on_device: bool, + pub is_result_on_device: bool, + pub is_async: bool, + pub ext: ConfigExtension, +} +``` + +#### Fields + +- **`stream_handle: IcicleStreamHandle`**: Specifies the stream (queue) to use for async execution +- **`is_a_on_device: bool`**: Indicates whether the input data a has been preloaded on the device memory. If `false` inputs will be copied from host to device. +- **`is_b_on_device: bool`**: Indicates whether the input b data has been preloaded on the device memory. If `false` inputs will be copied from host to device. +- **`is_result_on_device: bool`**: Indicates whether the output data is preloaded in device memory. If `false` outputs will be copied from host to device. +- **`is_async: bool`**: Specifies whether the NTT operation should be performed asynchronously. +- **`ext: ConfigExtension`**: extended configuration for backend. + +### Default Configuration + +`VecOpsConfig` can be initialized with default settings tailored for a specific device: + +```rust +let cfg = VecOpsConfig::default(); +``` + +## Vector Operations + +Vector operations are implemented through the `VecOps` trait, providing methods for addition, subtraction, and multiplication of vectors. + +### Methods + +All operations are element-wise operations, and the results placed into the `result` param. These operations are not in place. + +- **`add`**: Computes the element-wise sum of two vectors. +- **`accumulate`**: Sum input b to a inplace. +- **`sub`**: Computes the element-wise difference between two vectors. +- **`mul`**: Performs element-wise multiplication of two vectors. +- **`transpose`**: Performs matrix transpose. +- **`bit_reverse/bit_reverse_inplace`**: Reverse order of elements based on bit-reverse. + + + +```rust +pub fn add_scalars( + a: &(impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + result: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn accumulate_scalars( + a: &mut (impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn sub_scalars( + a: &(impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + result: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn mul_scalars( + a: &(impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + result: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn transpose_matrix( + input: &(impl HostOrDeviceSlice + ?Sized), + nof_rows: u32, + nof_cols: u32, + output: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn bit_reverse( + input: &(impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, + output: &mut (impl HostOrDeviceSlice + ?Sized), +) -> Result<(), eIcicleError>; + +pub fn bit_reverse_inplace( + input: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; +``` \ No newline at end of file diff --git a/docs/versioned_docs/version-3.0.0/introduction.md b/docs/versioned_docs/version-3.0.0/introduction.md new file mode 100644 index 000000000..b173cf5f9 --- /dev/null +++ b/docs/versioned_docs/version-3.0.0/introduction.md @@ -0,0 +1,42 @@ +--- +slug: / +displayed_sidebar: GettingStartedSidebar +title: '' +--- + +# Welcome to Ingonyama's Developer Documentation + +Ingonyama is a next-generation semiconductor company focusing on Zero-Knowledge Proof hardware acceleration. We build accelerators for advanced cryptography, unlocking real-time applications. Our focus is on democratizing access to compute-intensive cryptography and making it accessible for developers to build upon. + +Our flagship product is **ICICLE** + +#### **ICICLE v3** +[ICICLE v3](https://github.com/ingonyama-zk/icicle) is a versatile cryptography library designed to support multiple compute backends, including CUDA, CPU, and potentially others like Metal, WebGPU, Vulkan, and ZPU. Originally focused on GPU acceleration, ICICLE has evolved to offer backend-agnostic cryptographic acceleration, allowing you to build ZK provers or other cryptographic applications with ease, leveraging the best available hardware for your needs. + +- **Multiple Backend Support:** Develop on CPU and deploy on various backends including CUDA and potentially Metal, WebGPU, Vulkan, ZPU, or even remote machines. +- **Cross-Language Compatibility:** Use ICICLE across multiple programming languages such as C++, Rust, Go, and possibly Python. +- **Optimized for ZKPs:** Accelerate cryptographic operations like elliptic curve operations, MSM, NTT, Poseidon hash, and more. + +**Learn more about ICICLE and its multi-backend support [here][ICICLE-OVERVIEW].** + +--- + +## Our Approach to Hardware Acceleration + +We believe that GPUs are as critical for ZK as they are for AI. + +- **Parallelism:** Approximately 97% of ZK protocol runtime is naturally parallel, making GPUs an ideal match. +- **Developer-Friendly:** GPUs offer simplicity in scaling and usage compared to other hardware platforms. +- **Cost-Effective:** GPUs provide a competitive balance of power, performance, and cost, often being 3x cheaper than FPGAs. + +For a more in-depth understanding on this topic we suggest you read [our article on the subject](https://www.ingonyama.com/blog/revisiting-paradigm-hardware-acceleration-for-zero-knowledge-proofs). + + +## Get in Touch + +If you have any questions, ideas, or are thinking of building something in this space, join the discussion on [Discord]. You can explore our code on [github](https://github.com/ingonyama-zk) or read some of [our research papers](https://github.com/ingonyama-zk/papers). + +Follow us on [Twitter](https://x.com/Ingo_zk) and [YouTube](https://www.youtube.com/@ingo_ZK) and join us IRL at our [next event](https://www.ingonyama.com/events) + +[ICICLE-OVERVIEW]: ./icicle/overview.md +[Discord]: https://discord.gg/6vYrE7waPj diff --git a/docs/versioned_docs/version-3.1.0/contributor-guide.md b/docs/versioned_docs/version-3.1.0/contributor-guide.md new file mode 100644 index 000000000..ffd82cd9c --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/contributor-guide.md @@ -0,0 +1,23 @@ +# Contributor's Guide + +We welcome all contributions with open arms. At Ingonyama we take a village approach, believing it takes many hands and minds to build a ecosystem. + +## Contributing to ICICLE + +- Make suggestions or report bugs via [GitHub issues](https://github.com/ingonyama-zk/icicle/issues) +- Contribute to the ICICLE by opening a [pull request](https://github.com/ingonyama-zk/icicle/pulls). +- Contribute to our [documentation](https://github.com/ingonyama-zk/icicle/tree/main/docs) and [examples](https://github.com/ingonyama-zk/icicle/tree/main/examples). +- Ask questions on Discord + +### Opening a pull request + +When opening a [pull request](https://github.com/ingonyama-zk/icicle/pulls) please keep the following in mind. + +- `Clear Purpose` - The pull request should solve a single issue and be clean of any unrelated changes. +- `Clear description` - If the pull request is for a new feature describe what you built, why you added it and how its best that we test it. For bug fixes please describe the issue and the solution. +- `Consistent style` - Rust and Golang code should be linted by the official linters (golang fmt and rust fmt) and maintain a proper style. For CUDA and C++ code we use [`clang-format`](https://github.com/ingonyama-zk/icicle/blob/main/.clang-format), [here](https://github.com/ingonyama-zk/icicle/blob/605c25f9d22135c54ac49683b710fe2ce06e2300/.github/workflows/main-format.yml#L46) you can see how we run it. +- `Minimal Tests` - please add test which cover basic usage of your changes . + +## Questions? + +Find us on [Discord](https://discord.gg/6vYrE7waPj). diff --git a/docs/versioned_docs/version-3.1.0/grants.md b/docs/versioned_docs/version-3.1.0/grants.md new file mode 100644 index 000000000..1647d5400 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/grants.md @@ -0,0 +1,23 @@ +# Ingonyama Grant programs + +Ingonyama understands the importance of supporting and fostering a vibrant community of researchers and builders to advance ZK. To encourage progress, we are not only developing in the open but also sharing resources with researchers and builders through various programs. + +## ICICLE ZK-GPU Ecosystem Grant + +Ingonyama invites researchers and practitioners to collaborate in advancing ZK acceleration. We are allocating $100,000 for grants to support this initiative. + +### Bounties & Grants + +Eligibility for grants includes: + +1. **Students**: Utilize ICICLE in your research. +2. **Performance Improvement**: Enhance the performance of accelerated primitives in ICICLE. +3. **Protocol Porting**: Migrate existing ZK protocols to ICICLE. +4. **New Primitives**: Contribute new primitives to ICICLE. +5. **Benchmarking**: Compare ZK benchmarks against ICICLE. + +## Contact + +For questions or submissions: [grants@ingonyama.com](mailto:grants@ingonyama.com) + +**Read the full article [here](https://www.ingonyama.com/blog/icicle-for-researchers-grants-challenges)** diff --git a/docs/versioned_docs/version-3.1.0/icicle/arch_overview.md b/docs/versioned_docs/version-3.1.0/icicle/arch_overview.md new file mode 100644 index 000000000..a45f96a9b --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/arch_overview.md @@ -0,0 +1,27 @@ +# Architecture Overview + + +ICICLE v3 is designed with flexibility and extensibility in mind, offering a robust framework that supports multiple compute backends and accommodates various cryptographic needs. This section provides an overview of ICICLE's architecture, highlighting its open and closed components, multi-device support, and extensibility. + +### Frontend and CPU Backend + +- **Frontend (FE):** The ICICLE frontend is open-source and designed to provide a unified API across different programming languages, including C++, Rust, and Go. This frontend abstracts the complexity of working with different backends, allowing developers to write backend-agnostic code that can be deployed across various platforms. +- **CPU Backend:** ICICLE includes an open-source CPU backend that allows for development and testing on standard hardware. This backend is ideal for prototyping and for environments where specialized hardware is not available. + +## CUDA Backend + +- **CUDA Backend:** ICICLE also includes a high-performance CUDA backend that is closed-source. This backend is optimized for NVIDIA GPUs and provides significant acceleration for cryptographic operations. +- **Installation and Licensing:** The CUDA backend needs to be downloaded and installed. Refer to the [installation guide](./install_cuda_backend.md) for detailed instructions. + +## Multi-Device Support + +- **Scalability:** ICICLE supports multi-device configurations, enabling the distribution of workloads across multiple GPUs or other hardware accelerators. This feature allows for scaling ZK proofs and other cryptographic operations across larger data centers or high-performance computing environments. + + +## Build Your Own Backend + +ICICLE is designed to be extensible, allowing developers to integrate new backends or customize existing ones to suit their specific needs. The architecture supports: + +- **Custom Backends:** Developers can create their own backends to leverage different hardware or optimize for specific use cases. The process of building and integrating a custom backend is documented in the [Build Your Own Backend](./build_your_own_backend.md) section. +- **Pluggable Components:** ICICLE's architecture allows for easy integration of additional cryptographic primitives or enhancements, ensuring that the framework can evolve with the latest advancements in cryptography and hardware acceleration. + diff --git a/docs/versioned_docs/version-3.1.0/icicle/benchmarks.md b/docs/versioned_docs/version-3.1.0/icicle/benchmarks.md new file mode 100644 index 000000000..4a9ee6a08 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/benchmarks.md @@ -0,0 +1,3 @@ +# Benchmarks + +TODO \ No newline at end of file diff --git a/docs/versioned_docs/version-3.1.0/icicle/build_from_source.md b/docs/versioned_docs/version-3.1.0/icicle/build_from_source.md new file mode 100644 index 000000000..c8f439506 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/build_from_source.md @@ -0,0 +1,179 @@ + +# Build ICICLE from source + +This guide will help you get started with building, testing, and installing ICICLE, whether you're using C++, Rust, or Go. It also covers installation of the CUDA backend and important build options. + +## Building and Testing ICICLE frontend + +### C++: Build, Test, and Install (Frontend) + +ICICLE can be built and tested in C++ using CMake. The build process is straightforward, but there are several flags you can use to customize the build for your needs. + +#### Build Commands + +1. **Clone the ICICLE repository:** + ```bash + git clone https://github.com/ingonyama-zk/icicle.git + cd icicle + ``` + +2. **Configure the build:** + ```bash + mkdir -p build && rm -rf build/* + cmake -S icicle -B build -DFIELD=babybear + ``` + +:::info +To specify the field, use the flag -DFIELD=field, where field can be one of the following: babybear, stark252, m31. + +To specify a curve, use the flag -DCURVE=curve, where curve can be one of the following: bn254, bls12_377, bls12_381, bw6_761, grumpkin. +::: + +:::tip +If you have access to cuda backend repo, it can be built along ICICLE frontend by adding the following to the cmake command +- `-DCUDA_BACKEND=local` # if you have it locally +- `-DCUDA_BACKEND=` # to pull CUDA backend, given you have access +::: + +3. **Build the project:** + ```bash + cmake --build build -j + ``` + This is building the [libicicle_device](./libraries.md#icicle-device) and the [libicicle_field_babybear](./libraries.md#icicle-core) frontend lib that correspond to the field or curve. + +4. **Link:** +Link you application (or library) to ICICLE: +```cmake +target_link_libraries(yourApp PRIVATE icicle_field_babybear icicle_device) +``` + +5. **Installation (optional):** +To install the libs, specify the install prefix in the [cmake command](./build_from_source.md#build-commands) +`-DCMAKE_INSTALL_PREFIX=/install/dir/`. Default install path on linux is `/usr/local` if not specified. For other systems it may differ. The cmake command will print it to the log +``` +-- CMAKE_INSTALL_PREFIX=/install/dir/for/cmake/install +``` +Then after building, use cmake to install the libraries: +``` +cmake -S icicle -B build -DFIELD=babybear -DCMAKE_INSTALL_PREFIX=/path/to/install/dir/ +cmake --build build -j # build +cmake --install build # install icicle to /path/to/install/dir/ +``` + +6. **Run tests (optional):** +Add `-DBUILD_TESTS=ON` to the [cmake command](./build_from_source.md#build-commands) and build. +Execute all tests +```bash +cmake -S icicle -B build -DFIELD=babybear -DBUILD_TESTS=ON +cmake --build build -j +cd build/tests +ctest +``` +or choose the test-suite +```bash +./build/tests/test_field_api # or another test suite +# can specify tests using regex. For example for tests with ntt in the name: +./build/tests/test_field_api --gtest_filter="*ntt*" +``` +:::note +Most tests assume a cuda backend exists and will fail otherwise if cannot find a CUDA device. +::: + +#### Build Flags + +You can customize your ICICLE build with the following flags: + +- `-DCPU_BACKEND=ON/OFF`: Enable or disable built-in CPU backend. `default=ON`. +- `-DCMAKE_INSTALL_PREFIX=/install/dir`: Specify install directory. `default=/usr/local`. +- `-DBUILD_TESTS=ON/OFF`: Enable or disable tests. `default=OFF`. +- `-DBUILD_BENCHMARKS=ON/OFF`: Enable or disable benchmarks. `default=OFF`. + +#### Features + +By default, all [features](./libraries.md#supported-curves-and-operations) are enabled. +This is since installed backends may implement and register all APIs. Missing APIs in the frontend would cause linkage to fail due to missing symbols. Therefore by default we include them in the frontend part too. + +To disable features, add the following to the cmake command. +- ntt: `-DNTT=OFF` +- msm: `-DMSM=OFF` +- g2 msm: `-DG2=OFF` +- ecntt: `-DECNTT=OFF` +- extension field: `-DEXT_FIELD=OFF` + +:::tip +Disabling features is useful when developing with a backend that is slow to compile (e.g. CUDA backend); +::: + +### Rust: Build, Test, and Install + +To build and test ICICLE in Rust, follow these steps: + +1. **Navigate to the Rust bindings directory:** +```bash +cd wrappers/rust # or go to a specific field/curve 'cd wrappers/rust/icicle-fields/icicle-babybear' +``` + +2. **Build the Rust project:** +```bash +cargo build --release +``` +By default, all [supported features are enabled](#features). +Cargo features are used to disable features, rather than enable them, for the reason explained [here](#features): +- `no_g2` to disable G2 MSM +- `no_ecntt` to disable ECNTT + +They can be disabled as follows: +```bash +cargo build --release --features=no_ecntt,no_g2 +``` + +:::note +If you have access to cuda backend repo, it can be built along ICICLE frontend by using the following cargo features: +- `cuda_backend` : if the cuda backend resides in `icicle/backend/cuda` +- `pull_cuda_backend` : to pull main branch and build it +::: + + +3. **Run tests:** +```bash +cargo test # optional: --features=no_ecntt,no_g2,cuda_backend +``` +:::note +Most tests assume a CUDA backend is installed and fail otherwise. +::: + +4. **Install the library:** + +By default, the libraries are installed to the `target//deps/icicle` dir. If you want them installed elsewhere, define the env variable: +```bash +export ICICLE_INSTALL_DIR=/path/to/install/dir +``` + +#### Use as cargo dependency +In cargo.toml, specify the ICICLE libs to use: + +```bash +[dependencies] +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-babybear = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +# add other ICICLE crates here if need additional fields/curves +``` + +Can specify `branch = ` or `tag = ` or `rev = `. + +To disable features: +```bash +icicle-bls12-377 = { git = "https://github.com/ingonyama-zk/icicle.git", features = ["no_g2"] } +``` + +As explained above, the libs will be built and installed to `target//deps/icicle` so you can easily link to them. Alternatively you can set `ICICLE_INSTALL_DIR` env variable for a custom install directory. + +:::warning +Make sure to install icicle libs when installing a library/application that depends on icicle such that it is located at runtime. +::: + +### Go: Build, Test, and Install (TODO) + +--- +**To install CUDA backend and license click [here](./install_cuda_backend.md#installation)** diff --git a/docs/versioned_docs/version-3.1.0/icicle/build_your_own_backend.md b/docs/versioned_docs/version-3.1.0/icicle/build_your_own_backend.md new file mode 100644 index 000000000..5cb2fc52a --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/build_your_own_backend.md @@ -0,0 +1,3 @@ +# Build Your Own Backend + +TODO \ No newline at end of file diff --git a/docs/versioned_docs/version-3.1.0/icicle/colab-instructions.md b/docs/versioned_docs/version-3.1.0/icicle/colab-instructions.md new file mode 100644 index 000000000..5ed744362 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/colab-instructions.md @@ -0,0 +1,162 @@ +# Run ICICLE on Google Colab + +Google Colab lets you use a GPU free of charge, it's an Nvidia T4 GPU with 16 GB of memory, capable of running latest CUDA (tested on Cuda 12.2) +As Colab is able to interact with shell commands, a user can also install a framework and load git repositories into Colab space. + +## Prepare Colab environment + +First thing to do in a notebook is to set the runtime type to a T4 GPU. + +- in the upper corner click on the dropdown menu and select "change runtime type" + +![Change runtime](/img/colab_change_runtime.png) + +- In the window select "T4 GPU" and press Save + +![T4 GPU](/img/t4_gpu.png) + +Installing Rust is rather simple, just execute the following command: + +```sh +!apt install rustc cargo +``` + +To test the installation of Rust: + +```sh +!rustc --version +!cargo --version +``` + +A successful installation will result in a rustc and cargo version print, a faulty installation will look like this: + +```sh +/bin/bash: line 1: rustc: command not found +/bin/bash: line 1: cargo: command not found +``` + +Now we will check the environment: + +```sh +!nvcc --version +!gcc --version +!cmake --version +!nvidia-smi +``` + +A correct environment should print the result with no bash errors for `nvidia-smi` command and result in a **Teslt T4 GPU** type: + +```sh +nvcc: NVIDIA (R) Cuda compiler driver +Copyright (c) 2005-2023 NVIDIA Corporation +Built on Tue_Aug_15_22:02:13_PDT_2023 +Cuda compilation tools, release 12.2, V12.2.140 +Build cuda_12.2.r12.2/compiler.33191640_0 +gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 +Copyright (C) 2021 Free Software Foundation, Inc. +This is free software; see the source for copying conditions. There is NO +warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. + +cmake version 3.27.9 + +CMake suite maintained and supported by Kitware (kitware.com/cmake). +Wed Jan 17 13:10:18 2024 ++---------------------------------------------------------------------------------------+ +| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 | +|-----------------------------------------+----------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+======================+======================| +| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 | +| N/A 39C P8 9W / 70W | 0MiB / 15360MiB | 0% Default | +| | | N/A | ++-----------------------------------------+----------------------+----------------------+ + ++---------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=======================================================================================| +| No running processes found | ++---------------------------------------------------------------------------------------+ +``` + +## Cloning ICICLE and running test + +Now we are ready to clone ICICE repository, + +```sh +!git clone https://github.com/ingonyama-zk/icicle.git +``` + +We can browse the repository and run tests to check the runtime environment: + +```sh +!ls -la +%cd /content/icicle +``` + +## Download CUDA backend + +First let's create a backend directory + +```sh +%cd /content +!rm -rf cuda_backend/ +!mkdir cuda_backend +%cd cuda_backend +``` + +Download and extract a backend from [ICICLE released](https://github.com/ingonyama-zk/icicle/releases) backends +In this example we are using [ICICLE Cuda backend v3.1.0](https://github.com/ingonyama-zk/icicle/releases/download/v3.1.0/icicle_3_1_0-ubuntu22-cuda122.tar.gz) + +```sh +!curl -O -L https://github.com/ingonyama-zk/icicle/releases/download/v3.1.0/icicle_3_1_0-ubuntu22-cuda122.tar.gz +!tar -xvf icicle_3_1_0-ubuntu22-cuda122.tar.gz +``` + +## Setting CUDA backend installation directory +Point colab to the extracted cuda backend using an [environment variable](https://github.com/ingonyama-zk/icicle/blob/f638e9d3056d2a5d6271a67ba4f63973a2ba2c1a/docs/docs/icicle/getting_started.md#backend-loading) + +```sh +import os +os.envvar["ICICLE_BACKEND_INSTALL_DIR"] = "/content/cuda_backend/icicle" +``` + +## Fun with ICICLE + +Let's run a test! +Navigate to icicle/wrappers/rust/icicle-curves/icicle-bn254 and run cargo test: + +```sh +%cd /content/icicle/wrappers/rust/icicle-curves/icicle-bn254/ +!cargo test --release -- ntt +``` + +:::note + +Compiling the first time may take a while + +::: + +Test run should end like this: + +```sh +running 9 tests +[WARNING] Defaulting to Ingonyama icicle-cuda-license-server at `5053@license.icicle.ingonyama.com`. For more information about icicle-cuda-license, please contact support@ingonyama.com. +[INFO] ICICLE backend loaded from $ICICLE_BACKEND_INSTALL_DIR=/content/cuda_backend/icicle +test ecntt::tests::test_ecntt::test_ecntt_batch ... ok +test ntt::tests::test_ntt ... ok +test ntt::tests::test_ntt_arbitrary_coset ... ok +test ntt::tests::test_ntt_batch ... ok +test ntt::tests::test_ntt_coset_from_subgroup ... ok +test ntt::tests::test_ntt_coset_interpolation_nm ... ok +test ecntt::tests::test_ecntt::test_ecntt ... ok +test ntt::tests::test_ntt_device_async ... ok +test ntt::tests::test_ntt_release_domain ... ok + +test result: ok. 9 passed; 0 failed; 0 ignored; 0 measured; 36 filtered out; finished in 42.71s +``` + +Viola, ICICLE in Colab! diff --git a/docs/versioned_docs/version-3.1.0/icicle/faq_and_troubleshooting.md b/docs/versioned_docs/version-3.1.0/icicle/faq_and_troubleshooting.md new file mode 100644 index 000000000..79d07f5e6 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/faq_and_troubleshooting.md @@ -0,0 +1,9 @@ +# FAQ and troubleshooting + +## Frequently asked questions + +TODO + +## Troubleshooting + +TODO \ No newline at end of file diff --git a/docs/versioned_docs/version-3.1.0/icicle/getting_started.md b/docs/versioned_docs/version-3.1.0/icicle/getting_started.md new file mode 100644 index 000000000..a08111092 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/getting_started.md @@ -0,0 +1,170 @@ +# Getting started Guide + +## Overview + +This guide will walk you through the entire process of building, testing, and installing ICICLE using your preferred programming language: C++, Rust, or Go. Whether you're deploying on a CPU or leveraging CUDA for accelerated performance, this guide provides comprehensive instructions to get you started. It also outlines the typical workflow for a user, including key installation steps: + + +1. **Install ICICLE or build it from source**: This is explained in this guide. For building from source, refer to the [Build from Source page](./build_from_source.md). +2. **Follow the [Programmer’s Guide](./programmers_guide/general.md)**: Learn how to use ICICLE APIs. +3. **Start using ICICLE APIs on your CPU**: Your application will now use ICICLE on the CPU. +4. **Accelerate your application on a GPU**: [install the CUDA backend](./install_cuda_backend.md), load it, and select it in your application ([C++](./programmers_guide/cpp.md#loading-a-backend),[Rust](./programmers_guide/rust.md#loading-a-backend), [Go](./programmers_guide/go.md#loading-a-backend)). +5. **Run on the GPU**: Once the GPU backend is selected, all subsequent API calls will execute on the GPU. +6. **Optimize for multi-GPU environments**: Refer to the [Multi-GPU](./multi-device.md) Guide to fully utilize your system’s capabilities. +7. **Review memory management**: Revisit the [Memory Management section](./programmers_guide/general.md#device-abstraction) to allocate memory on the device efficiently and try to keep data on the GPU as long as possible. + + +The rest of this page details the content of a release, how to install it, and how to use it. ICICLE binaries are released for multiple Linux distributions, including Ubuntu 20.04, Ubuntu 22.04, RHEL 8, and RHEL 9. + +:::note +Future releases will also include support for macOS and other systems. +::: + +## Content of a Release + +Each ICICLE release includes a tar file named `icicle30-.tar.gz`, where `icicle30` indicates version 3.0. This tar file contains ICICLE frontend build artifacts and headers for a specific distribution. The tar file structure includes: + +- **`./icicle/include/`**: This directory contains all the necessary header files for using the ICICLE library from C++. +- **`./icicle/lib/`**: + - **Icicle Libraries**: All the core ICICLE libraries are located in this directory. Applications linking to ICICLE will use these libraries. + - **Backends**: The `./icicle/lib/backend/` directory houses backend libraries, including the CUDA backend (not included in this tar). + +- **CUDA backend** comes as separate tar `icicle30--cuda122.tar.gz` + - per distribution, for ICICLE-frontend v3.0 and CUDA 12.2. + +## Installing and using ICICLE + +- [Full C++ example](https://github.com/ingonyama-zk/icicle/tree/main/examples/c++/install-and-use-icicle) +- [Full Rust example](https://github.com/ingonyama-zk/icicle/tree/main/examples/rust/install-and-use-icicle) +- [Full Go example](https://github.com/ingonyama-zk/icicle/tree/main/examples/golang/install-and-use-icicle) + +1. **Extract and install the Tar Files**: + - [Download](https://github.com/ingonyama-zk/icicle/releases) the appropriate tar files for your distribution (Ubuntu 20.04, Ubuntu 22.04, or UBI 8,9 for RHEL compatible binaries). + - **Frontend libs and headers** should be installed in default search paths (such as `/usr/lib` and `usr/local/include`) for the compiler and linker to find. + - **Backend libs** should be installed in `/opt` + - Extract it to your desired location: + ```bash + # install the frontend part (Can skip for Rust) + tar xzvf icicle30-ubuntu22.tar.gz + cp -r ./icicle/lib/* /usr/lib/ + cp -r ./icicle/include/icicle/ /usr/local/include/ # copy C++ headers + # extract CUDA backend (OPTIONAL) + tar xzvf icicle30-ubuntu22-cuda122.tar.gz -C /opt + ``` + + :::note + Installing the frontend is optional for Rust. Rust does not use it. + ::: + + :::tip + You may install to any directory, but you need to ensure it can be found by the linker at compile and runtime. + You can install anywhere and use a symlink to ensure it can be easily found as if it were in the default directory. + ::: + +2. **Linking Your Application**: + + Applications need to link to the ICICLE device library and to every field and/or curve library. The backend libraries are dynamically loaded at runtime, so there is no need to link to them. + + **C++** + - When compiling your C++ application, link against the ICICLE libraries: + ```bash + g++ -o myapp myapp.cpp -licicle_device -licicle_field_bn254 -licicle_curve_bn254 + # if not installed in standard dirs, for example /custom/path/, need to specify it + g++ -o myapp myapp.cpp -I/custom/path/icicle/include -L/custom/path/icicle/lib -licicle_device -licicle_field_bn254 -licicle_curve_bn254 -Wl,-rpath,/custom/path/icicle/lib/ + ``` + + - Or via cmake + ```bash + # Add the executable + add_executable(example example.cpp) + # Link the libraries + target_link_libraries(example icicle_device icicle_field_bn254 icicle_curve_bn254) + + # OPTIONAL (if not installed in default location) + + # The following is setting compile and runtime paths for headers and libs assuming + # - headers in /custom/path/icicle/include + # - libs in/custom/path/icicle/lib + + # Include directories + target_include_directories(example PUBLIC /custom/path/icicle/include) + # Library directories + target_link_directories(example PUBLIC /custom/path/icicle/lib/) + # Set the RPATH so linker finds icicle libs at runtime + set_target_properties(example PROPERTIES + BUILD_RPATH /custom/path/icicle/lib/ + INSTALL_RPATH /custom/path/icicle/lib/) + ``` + + :::tip + If you face linkage issues, try `ldd myapp` to see the runtime dependencies. If ICICLE libs are not found, you need to add the install directory to the search path of the linker. In a development environment, you can do that using the environment variable export `LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/custom/path/icicle/lib` or similar (for non-Linux). For deployment, make sure it can be found and avoid using LD_LIBRARY_PATH. + + Alternatively, you can embed the search path in the app as an rpath by adding `-Wl,-rpath,/custom/path/icicle/lib/`. This is demonstrated above. + ::: + + **Rust** + - When building the ICICLE crates, ICICLE frontend libs are built from source, along with the Rust bindings. They are installed to `target//deps/icicle`, and Cargo will link them correctly. Note that you still need to install the CUDA backend if you have a CUDA GPU. + - Simply use `cargo build` or `cargo run` and it should link to ICICLE libs. + + **Go** - TODO + +:::warning +When deploying an application (whether in C++, Rust, or Go), you must make sure to either deploy the ICICLE libs (that you download or build from source) along with the application binaries (as tar, Docker image, package manager installer, or otherwise) or make sure to install ICICLE (and the backend) on the target machine. Otherwise, the target machine will have linkage issues. +::: + +## Backend Loading + +The ICICLE library dynamically loads backend libraries at runtime. By default, it searches for backends in the following order: + +1. **Environment Variable**: If the `ICICLE_BACKEND_INSTALL_DIR` environment variable is defined, ICICLE will prioritize this location. +2. **Default Directory**: If the environment variable is not set, Icicle will search in the default directory `/opt/icicle/lib/backend`. + +:::warning +If building ICICLE frontend from source, make sure to load a backend that is compatible with the frontend version. CUDA backend libs are forward compatible with newer frontends (e.g., CUDA-backend-3.0 works with ICICLE-3.2). The opposite is not guaranteed. +::: + +If you install in a custom dir, make sure to set `ICICLE_BACKEND_INSTALL_DIR`: +```bash +ICICLE_BACKEND_INSTALL_DIR=path/to/icicle/lib/backend/ myapp # for an executable myapp +ICICLE_BACKEND_INSTALL_DIR=path/to/icicle/lib/backend/ cargo run # when using cargo +``` + +Then to load backend from ICICLE_BACKEND_INSTALL_DIR or `/opt/icicle/lib/backend` in your application: + +**C++** +```cpp +extern "C" eIcicleError icicle_load_backend_from_env_or_default(); +``` +**Rust** +```rust +pub fn load_backend_from_env_or_default() -> Result<(), eIcicleError>; +``` +**Go** +```go +func LoadBackendFromEnvOrDefault() EIcicleError +``` + +### Custom Backend Loading + +If you need to load a backend from a custom location at any point during runtime, you can call the following function: + +**C++** +```cpp +extern "C" eIcicleError icicle_load_backend(const char* path, bool is_recursive); +``` +- **`path`**: The directory where the backend libraries are located. +- **`is_recursive`**: If `true`, the function will search for backend libraries recursively within the specified path. + +**Rust** +```rust + pub fn load_backend(path: &str) -> Result<(), eIcicleError>; // OR + pub fn load_backend_non_recursive(path: &str) -> Result<(), eIcicleError>; +``` +- **`path`**: The directory where the backend libraries are located. + +**Go** +```go +func LoadBackend(path string, isRecursive bool) EIcicleError +``` +- **`path`**: The directory where the backend libraries are located. +- **`isRecursive`**: If `true`, the function will search for backend libraries recursively within the specified path. diff --git a/docs/versioned_docs/version-3.1.0/icicle/golang-bindings.md b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings.md new file mode 100644 index 000000000..87f95a439 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings.md @@ -0,0 +1,126 @@ +# Golang bindings + +Golang bindings allow you to use ICICLE as a golang library. +The source code for all Golang packages can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang). + +The Golang bindings are comprised of multiple packages. + +[`core`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/core) which defines all shared methods and structures, such as configuration structures, or memory slices. + +[`runtime`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/runtime) which defines abstractions for ICICLE methods for allocating memory, initializing and managing streams, and `Device` which enables users to define and keep track of devices. + +Each supported curve and field has its own package which you can find in the respective directories [here](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang). If your project uses BN254 you only need to import that single package named [`bn254`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/curves/bn254). + +## Using ICICLE Golang bindings in your project + +To add ICICLE to your `go.mod` file. + +```bash +go get github.com/ingonyama-zk/icicle/v3 +``` + +If you want to specify a specific branch + +```bash +go get github.com/ingonyama-zk/icicle/v3@ +``` + +For a specific commit + +```bash +go get github.com/ingonyama-zk/icicle/v3@ +``` + +### Building from source + +To build the shared libraries you can run [this](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/golang/build.sh) script inside the downloaded go dependency: + +```sh +./build.sh [-curve=] [-field=] [-cuda_version=] [-skip_msm] [-skip_ntt] [-skip_g2] [-skip_ecntt] [-skip_fieldext] + +curve - The name of the curve to build or "all" to build all supported curves +field - The name of the field to build or "all" to build all supported fields +-skip_msm - Optional - build with MSM disabled +-skip_ntt - Optional - build with NTT disabled +-skip_g2 - Optional - build with G2 disabled +-skip_ecntt - Optional - build with ECNTT disabled +-skip_fieldext - Optional - build without field extension +-help - Optional - Displays usage information +``` + +:::note + +If more than one curve or more than one field is supplied, the last one supplied will be built + +::: + +To build ICICLE libraries for all supported curves without certain features, you can use their -skip_\ flags. For example, for disabling G2 and ECNTT: + +```bash +./build.sh -curve=all -skip_g2 -skip_ecntt +``` + +By default, all features are enabled. To build for a specific field or curve, you can pass the `-field=` or `-curve=` flags: + +``` bash +./build.sh -curve=bn254 +``` + +Now you can import ICICLE into your project + +```go +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) +... +``` + +### Building with precompiled libs + +Download the frontend release binaries from our [github release page](https://github.com/ingonyama-zk/icicle/releases), for example: icicle30-ubuntu22.tar.gz for ICICLE v3 on ubuntu 22.04 + +Extract the libs and move them to the downloaded go dependency in your GOMODCACHE + +```sh +# extract frontend part +tar xzvf icicle30-ubuntu22.tar.gz +cp -r ./icicle/lib/* $(go env GOMODCACHE)/github.com/ingonyama-zk/icicle/v3@/build/lib/ +``` + +## Running tests + +To run all tests, for all curves: + +```bash +go test ./... -count=1 +``` + +If you wish to run test for a specific curve or field: + +```bash +go test -count=1 +``` + +## How do Golang bindings work? + +The golang packages are binded to the libraries produced from compiling ICICLE using cgo. + +1. These libraries (named `libicicle_curve_.a` and `libicicle_field_.a`) can be imported in your Go project to leverage the accelerated functionalities provided by ICICLE. + +2. In your Go project, you can use `cgo` to link these libraries. Here's a basic example on how you can use `cgo` to link these libraries: + +```go +/* +#cgo LDFLAGS: -L/path/to/shared/libs -licicle_device -lstdc++ -lm -Wl,-rpath=/path/to/shared/libs +#include "icicle.h" // make sure you use the correct header file(s) +*/ +import "C" + +func main() { + // Now you can call the C functions from the ICICLE libraries. + // Note that C function calls are prefixed with 'C.' in Go code. +} +``` + +Replace `/path/to/shared/libs` with the actual path where the shared libraries are located on your system. diff --git a/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/ecntt.md b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/ecntt.md new file mode 100644 index 000000000..4fd1acf8e --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/ecntt.md @@ -0,0 +1,100 @@ +# ECNTT + +## ECNTT Method + +The `ECNtt[T any]()` function performs the Elliptic Curve Number Theoretic Transform (EC-NTT) on the input points slice, using the provided dir (direction), cfg (configuration), and stores the results in the results slice. + +```go +func ECNtt[T any](points core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) runtime.EIcicleError +``` + +### Parameters + +- **`points`**: A slice of elliptic curve points (in projective coordinates) that will be transformed. The slice can be stored on the host or the device, as indicated by the `core.HostOrDeviceSlice` type. +- **`dir`**: The direction of the EC-NTT transform, either `core.KForward` or `core.KInverse`. +- **`cfg`**: A pointer to an `NTTConfig` object, containing configuration options for the NTT operation. +- **`results`**: A slice that will store the transformed elliptic curve points (in projective coordinates). The slice can be stored on the host or the device, as indicated by the `core.HostOrDeviceSlice` type. + +### Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the EC-NTT operation was successful, or an error if something went wrong. + +## NTT Configuration (NTTConfig) + +The `NTTConfig` structure holds configuration parameters for the NTT operation, allowing customization of its behavior to optimize performance based on the specifics of your protocol. + +```go +type NTTConfig[T any] struct { + StreamHandle runtime.Stream + CosetGen T + BatchSize int32 + ColumnsBatch bool + Ordering Ordering + areInputsOnDevice bool + areOutputsOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler +} +``` + +### Fields + +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`CosetGen`**: Coset generator. Used to perform coset (i)NTTs. +- **`BatchSize`**: The number of NTTs to compute in one operation, defaulting to 1. +- **`ColumnsBatch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. +- **`Ordering`**: Ordering of inputs and outputs (`KNN`, `KNR`, `KRN`, `KRR`), affecting how data is arranged. +- **`areInputsOnDevice`**: Indicates if input scalars are located on the device. +- **`areOutputsOnDevice`**: Indicates if results are stored on the device. +- **`IsAsync`**: Controls whether the NTT operation runs asynchronously. +- **`Ext`**: Extended configuration for backend. + +### Default Configuration + +Use `GetDefaultNTTConfig` to obtain a default configuration, customizable as needed. + +```go +func GetDefaultNTTConfig[T any](cosetGen T) NTTConfig[T] +``` + +## ECNTT Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ecntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func Main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + // Obtain the default NTT configuration with a predefined coset generator. + cfg := ntt.GetDefaultNttConfig() + + // Define the size of the input scalars. + size := 1 << 18 + + // Generate Points for the ECNTT operation. + points := bn254.GenerateProjectivePoints(size) + + // Set the direction of the NTT (forward or inverse). + dir := core.KForward + + // Allocate memory for the results of the NTT operation. + results := make(core.HostSlice[bn254.Projective], size) + + // Perform the NTT operation. + err := ecntt.ECNtt(points, dir, &cfg, results) + if err != runtime.Success { + panic("ECNTT operation failed") + } +} +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/hash.md b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/hash.md new file mode 100644 index 000000000..8e05c9f46 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/hash.md @@ -0,0 +1,111 @@ +# ICICLE Hashing in Golang + +:::note + +For a general overview of ICICLE's hashing logic and supported algorithms, check out the [ICICLE Hashing Overview](../primitives/hash.md). + +::: + +:::caution Warning + +Using the Hash package requires `go` version 1.22 + +::: + +## Overview + +The ICICLE library provides Golang bindings for hashing using a variety of cryptographic hash functions. These hash functions are optimized for both general-purpose data and cryptographic operations such as multi-scalar multiplication, commitment generation, and Merkle tree construction. + +This guide will show you how to use the ICICLE hashing API in Golang with examples for common hash algorithms, such as Keccak-256, Keccak-512, SHA3-256, SHA3-512, Blake2s, and Poseidon. + +## Importing Hash Functions + +To use the hashing functions in Golang, you only need to import the hash package from the ICICLE Golang bindings. For example: + +```go +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/hash" +) +``` + +## API Usage + +### 1. Creating a Hasher Instance + +Each hash algorithm can be instantiated by calling its respective constructor. The `NewHasher` function takes an optional default input size, which can be set to 0 unless required for a specific use case. + +Example for Keccak-256: + +```go +keccakHasher := hash.NewKeccak256Hasher(0 /* default input size */) +``` + +### 2. Hashing a Simple String + +Once you have created a hasher instance, you can hash any input data, such as strings or byte arrays, and store the result in an output buffer. +Here’s how to hash a simple string using Keccak-256: + +```go +import ( + "encoding/hex" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/hash" +) + +inputStrAsBytes := []bytes("I like ICICLE! It's so fast and simple") +keccakHasher, error := hash.NewKeccak256Hasher(0 /*default chunk size*/) +if error != runtime.Success { + fmt.Println("error:", error) + return +} + +outputRef := make([]byte, 32) +keccakHasher.Hash( + core.HostSliceFromElements(inputStrAsBytes), + core.HostSliceFromElements(outputRef), + core.GetDefaultHashConfig(), +) + +// convert the output to a hex string for easy readability +outputAsHexStr = hex.EncodeToString(outputRef) +fmt.Println!("Hash(`", input_str, "`) =", &outputAsHexStr) +``` + +Using other hash algorithms is similar and only requires replacing the Hasher constructor with the relevant hashing algorithm. + +### 3. Poseidon Example (field elements) and batch hashing + +The Poseidon hash is designed for cryptographic field elements and curves, making it ideal for use cases such as zero-knowledge proofs (ZKPs). Poseidon hash using babybear field: + +:::note + +Since poseidon is designed for use with field elements and curves, it is located within the field or curve packages and not in the Hash package though it does rely on using the Hash package. + +::: + +```go +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + babybear "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear/poseidon" +) + +batch := 1 << 4 +t := 3 // Currently support arity of 3, 5, 9, 12 +// (t - 1) is due to domainTag being non nil +// if domainTag is nil, then the input size should be `batch * t` +// See more in our tests: https://github.com/ingonyama-zk/icicle/blob/docs/v3/golang/poseidon/wrappers/golang/curves/bn254/tests/poseidon_test.go#L23-L27 +inputsSize = batch * (t - 1) +inputs := babybear.GenerateScalars(inputsSize) +domainTag := babybear.GenerateScalars(1)[0] + +outputsRef := make([]babybear.ScalarField, batch) +poseidonHasherRef, _ := poseidon.NewHasher(uint64(t), &domainTag) +poseidonHasherRef.Hash( + core.HostSliceFromElements(inputs), + core.HostSliceFromElements(outputsRef), + core.GetDefaultHashConfig(), +) + +poseidonHasherRef.Delete() +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/merkle.md b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/merkle.md new file mode 100644 index 000000000..4e1cf9025 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/merkle.md @@ -0,0 +1,242 @@ +# Merkle Tree API Documentation (Golang) + +This is the Golang version of the **Merkle Tree API Documentation** ([C++ documentation](../primitives/merkle.md)). It mirrors the structure and functionality of the C++ version, providing equivalent APIs in Golang. +For more detailed explanations, refer to the [C++ documentation](../primitives/merkle.md). + +To see a complete implementation, visit the [Hash and Merkle example](https://github.com/ingonyama-zk/icicle/tree/main/examples/rust/hash-and-merkle) for a full example. + +:::caution Warning + +Using the Hash package requires `go` version 1.22 + +::: + +## Tree Structure and Configuration in Golang + +### Defining a Merkle Tree + +```go +/// * `layerHashers` - A vector of hash objects representing the hashers of each layer. +/// * `leafElementSize` - Size of each leaf element. +/// * `outputStoreMinLayer` - Minimum layer at which the output is stored. +/// +/// # Returns a new `MerkleTree` instance or EIcicleError. +func CreateMerkleTree( + layerHashers []hash.Hasher, + leafElementSize, + outputStoreMinLayer uint64, +) (MerkleTree, runtime.EIcicleError) +``` + +The `outputStoreMinLayer` parameter defines the lowest layer that will be stored in memory. Layers below this value will not be stored, saving memory at the cost of additional computation when proofs are generated. + +### Building the Tree + +The Merkle tree can be constructed from input data of any type, allowing flexibility in its usage. The size of the input must align with the tree structure defined by the hash layers and leaf size. If the input size does not match the expected size, padding may be applied. + +Refer to the [Padding Section](#padding) for more details on how mismatched input sizes are handled. + +```go +/// * `mt` - The merkle tree object to build +/// * `leaves` - A slice of leaves (input data). +/// * `config` - Configuration for the Merkle tree. +/// +/// # Returns a result indicating success or failure. +func BuildMerkleTree[T any]( + mt *MerkleTree, + leaves core.HostOrDeviceSlice, + cfg core.MerkleTreeConfig, +) runtime.EIcicleError +``` + +## Tree Examples in Golang + +### Example A: Binary Tree + +A binary tree with **5 layers**, using **Keccak-256**: + +![Merkle Tree Diagram](../primitives/merkle_diagrams/diagram1.png) + +```go +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/hash" + merkletree "github.com/ingonyama-zk/icicle/v3/wrappers/golang/merkle-tree" +) + +leafSize := 1024 +maxInputSize := leafSize * 16 +input := make([]byte, maxInputSize) + +hasher, _ := hash.NewKeccak256Hasher(uint64(leafSize)) +compress, _ := hash.NewKeccak256Hasher(2 * hasher.OutputSize()) +layerHashers := []hash.Hasher{hasher, compress, compress, compress, compress} + +mt, _ := merkletree.CreateMerkleTree(layerHashers, uint64(leafSize), 0 /* min layer to store */) + +merkletree.BuildMerkleTree[byte](&mt, core.HostSliceFromElements(input), core.GetDefaultMerkleTreeConfig()) +``` + +### Example B: Tree with Arity 4 + +![Merkle Tree Diagram](../primitives/merkle_diagrams/diagram2.png) + +This example uses **Blake2s** in upper layers: + +```go +// define layer hashers +// we want one hash layer to hash every 1KB to 32B then compress every 128B so only 2 more layers +hasher, _ := hash.NewKeccak256Hasher(uint64(leafSize)) +compress, _ := hash.NewBlake2sHasher(2 * hasher.OutputSize()) +layerHashers := []hash.Hasher{hasher, compress, compress,} + +mt, _ := merkletree.CreateMerkleTree(layerHashers, uint64(leafSize), 0 /* min layer to store */) + +merkletree.BuildMerkleTree[byte](&mt, core.HostSliceFromElements(input), core.GetDefaultMerkleTreeConfig()) +``` + +## Padding + +:::note +Padding feature is not yet supported in **v3.1** and is planned for **v3.2**. +::: + +When the input for **layer 0** is smaller than expected, ICICLE can apply **padding** to align the data. + +**Padding Schemes:** + +1. **Zero padding:** Adds zeroes to the remaining space. +2. **Repeat last leaf:** The final leaf element is repeated to fill the remaining space. + +```go +// type PaddingPolicy = int + +// const ( +// NoPadding PaddingPolicy = iota // No padding, assume input is correctly sized. +// ZeroPadding // Pad the input with zeroes to fit the expected input size. +// LastValuePadding // Pad the input by repeating the last value. +// ) + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" +) + +config := core.GetDefaultMerkleTreeConfig(); +config.PaddingPolicy = core.ZeroPadding; +merkletree.BuildMerkleTree[byte](&mt, core.HostSliceFromElements(input), core.GetDefaultMerkleTreeConfig()) +``` + +## Root as Commitment + +Retrieve the Merkle-root and serialize. + +```go +/// Retrieve the root of the Merkle tree. +/// +/// # Returns +/// A reference to the root hash. +func GetMerkleTreeRoot[T any](mt *MerkleTree) ([]T, runtime.EIcicleError) + +commitment := merkletree.GetMerkleTreeRoot[byte](&mt) +fmt.Println!("Commitment:", commitment) +``` + +:::warning +The commitment can be serialized to the proof. This is not handled by ICICLE. +::: + +## Generating Merkle Proofs + +Merkle proofs are used to **prove the integrity of opened leaves** in a Merkle tree. A proof ensures that a specific leaf belongs to the committed data by enabling the verifier to reconstruct the **root hash (commitment)**. + +A Merkle proof contains: + +- **Leaf**: The data being verified. +- **Index** (leaf_idx): The position of the leaf in the original dataset. +- **Path**: A sequence of sibling hashes (tree nodes) needed to recompute the path from the leaf to the root. + +![Merkle Pruned Phat Diagram](../primitives/merkle_diagrams/diagram1_path.png) + +```go +/// * `leaves` - A slice of leaves (input data). +/// * `leaf_idx` - Index of the leaf to generate a proof for. +/// * `pruned_path` - Whether the proof should be pruned. +/// * `config` - Configuration for the Merkle tree. +/// +/// # Returns a `MerkleProof` object or eIcicleError +func GetMerkleTreeProof[T any]( + mt *MerkleTree, + leaves core.HostOrDeviceSlice, + leafIndex uint64, + prunedPath bool, + cfg core.MerkleTreeConfig, +) (MerkleProof, runtime.EIcicleError) +``` + +### Example: Generating a Proof + +Generating a proof for leaf idx 5: + +```go +mp, _ := merkletree.GetMerkleTreeProof[byte]( + &mt, + core.HostSliceFromElements(input), + 5, /* leafIndex */ + true, /* prunedPath */ + core.GetDefaultMerkleTreeConfig(), +) +``` + +:::warning +The Merkle-path can be serialized to the proof along with the leaf. This is not handled by ICICLE. +::: + +## Verifying Merkle Proofs + +```go +/// * `proof` - The Merkle proof to verify. +/// +/// # Returns a result indicating whether the proof is valid. +func (mt *MerkleTree) Verify(mp *MerkleProof) (bool, runtime.EIcicleError) +``` + +### Example: Verifying a Proof + +```go +isVerified, err := mt.Verify(&mp) +assert.True(isVerified) +``` + +## Pruned vs. Full Merkle-paths + +A **Merkle path** is a collection of **sibling hashes** that allows the verifier to **reconstruct the root hash** from a specific leaf. +This enables anyone with the **path and root** to verify that the **leaf** belongs to the committed dataset. +There are two types of paths that can be computed: + +- [**Pruned Path:**](#generating-merkle-proofs) Contains only necessary sibling hashes. +- **Full Path:** Contains all sibling nodes and intermediate hashes. + +![Merkle Full Path Diagram](../primitives//merkle_diagrams/diagram1_path_full.png) + +To compute a full path, specify `pruned=false`: + +```go +mp, _ := merkletree.GetMerkleTreeProof[byte]( + &mt, + core.HostSliceFromElements(input), + 5, /* leafIndex */ + false, /*non-pruned is a full path --> note the pruned flag here*/ + core.GetDefaultMerkleTreeConfig(), +) +``` + +## Handling Partial Tree Storage + +In cases where the **Merkle tree is large**, only the **top layers** may be stored to conserve memory. +When opening leaves, the **first layers** (closest to the leaves) are **recomputed dynamically**. + +For example to avoid storing first layer we can define a tree as follows: + +```go +mt, err := merkletree.CreateMerkleTree(layerHashers, leafSize, 1 /*min layer to store*/); +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/msm-pre-computation.md b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/msm-pre-computation.md new file mode 100644 index 000000000..f13998d3e --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/msm-pre-computation.md @@ -0,0 +1,110 @@ +# MSM Pre computation + +To understand the theory behind MSM pre computation technique refer to Niall Emmart's [talk](https://youtu.be/KAWlySN7Hm8?feature=shared&t=1734). + +## Core package + +### MSM PrecomputeBases + +`PrecomputeBases` and `G2PrecomputeBases` exists for all supported curves. + +#### Description + +This function extends each provided base point $(P)$ with its multiples $(2^lP, 2^{2l}P, ..., 2^{(precompute_factor - 1) \cdot l}P)$, where $(l)$ is a level of precomputation determined by the `precompute_factor`. The extended set of points facilitates faster MSM computations by allowing the MSM algorithm to leverage precomputed multiples of base points, reducing the number of point additions required during the computation. + +The precomputation process is crucial for optimizing MSM operations, especially when dealing with large sets of points and scalars. By precomputing and storing multiples of the base points, the MSM function can more efficiently compute the scalar-point multiplications. + +#### `PrecomputeBases` + +Precomputes points for MSM by extending each base point with its multiples. + +```go +func PrecomputeBases(bases core.HostOrDeviceSlice, cfg *core.MSMConfig, outputBases core.DeviceSlice) runtime.EIcicleError +``` + +##### Parameters + +- **`bases`**: A slice of the original affine points to be extended with their multiples. +- **`cfg`**: The MSM configuration parameters. +- **`outputBases`**: The device slice allocated for storing the extended points. + +##### Example + +```go +package main + +import ( + "log" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + cfg := core.GetDefaultMSMConfig() + points := bn254.GenerateAffinePoints(1024) + cfg.PrecomputeFactor = 8 + var precomputeOut core.DeviceSlice + precomputeOut.Malloc(points[0].Size(), points.Len()*int(cfg.PrecomputeFactor)) + + err := msm.PrecomputeBases(points, &cfg, precomputeOut) + if err != runtime.Success { + log.Fatalf("PrecomputeBases failed: %v", err) + } +} +``` + +#### `G2PrecomputeBases` + +This method is the same as `PrecomputePoints` but for G2 points. Extends each G2 curve base point with its multiples for optimized MSM computations. + +```go +func G2PrecomputeBases(bases core.HostOrDeviceSlice, cfg *core.MSMConfig, outputBases core.DeviceSlice) runtime.EIcicleError +``` + +##### Parameters + +- **`bases`**: A slice of the original affine points to be extended with their multiples. +- **`cfg`**: The MSM configuration parameters. +- **`outputBases`**: The device slice allocated for storing the extended points. + +##### Example + +```go +package main + +import ( + "log" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/g2" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + cfg := core.GetDefaultMSMConfig() + points := g2.G2GenerateAffinePoints(1024) + cfg.PrecomputeFactor = 8 + var precomputeOut core.DeviceSlice + precomputeOut.Malloc(points[0].Size(), points.Len()*int(cfg.PrecomputeFactor)) + + err := g2.G2PrecomputeBases(points, &cfg, precomputeOut) + if err != runtime.Success { + log.Fatalf("PrecomputeBases failed: %v", err) + } +} +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/msm.md b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/msm.md new file mode 100644 index 000000000..7209eca47 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/msm.md @@ -0,0 +1,205 @@ +# MSM + +## MSM Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + // Obtain the default MSM configuration. + cfg := core.GetDefaultMSMConfig() + + // Define the size of the problem, here 2^18. + size := 1 << 18 + + // Generate scalars and points for the MSM operation. + scalars := bn254.GenerateScalars(size) + points := bn254.GenerateAffinePoints(size) + + // Create a CUDA stream for asynchronous operations. + stream, _ := runtime.CreateStream() + var p bn254.Projective + + // Allocate memory on the device for the result of the MSM operation. + var out core.DeviceSlice + _, e := out.MallocAsync(p.Size(), 1, stream) + + if e != runtime.Success { + panic(e) + } + + // Set the CUDA stream in the MSM configuration. + cfg.StreamHandle = stream + cfg.IsAsync = true + + // Perform the MSM operation. + e = msm.Msm(scalars, points, &cfg, out) + + if e != runtime.Success { + panic(e) + } + + // Allocate host memory for the results and copy the results from the device. + outHost := make(core.HostSlice[bn254.Projective], 1) + runtime.SynchronizeStream(stream) + runtime.DestroyStream(stream) + outHost.CopyFromDevice(&out) + + // Free the device memory allocated for the results. + out.Free() +} +``` + +## MSM Method + +```go +func Msm(scalars core.HostOrDeviceSlice, points core.HostOrDeviceSlice, cfg *core.MSMConfig, results core.HostOrDeviceSlice) runtime.EIcicleError +``` + +### Parameters + +- **`scalars`**: A slice containing the scalars for multiplication. It can reside either in host memory or device memory. +- **`points`**: A slice containing the points to be multiplied with scalars. Like scalars, these can also be in host or device memory. +- **`cfg`**: A pointer to an `MSMConfig` object, which contains various configuration options for the MSM operation. +- **`results`**: A slice where the results of the MSM operation will be stored. This slice can be in host or device memory. + +### Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. + +## MSMConfig + +The `MSMConfig` structure holds configuration parameters for the MSM operation, allowing customization of its behavior to optimize performance based on the specifics of the operation or the underlying hardware. + +```go +type MSMConfig struct { + StreamHandle runtime.Stream + PrecomputeFactor int32 + C int32 + Bitsize int32 + BatchSize int32 + ArePointsSharedInBatch bool + areScalarsOnDevice bool + AreScalarsMontgomeryForm bool + areBasesOnDevice bool + AreBasesMontgomeryForm bool + areResultsOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler +} +``` + +### Fields + +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`PrecomputeFactor`**: Controls the number of extra points to pre-compute. +- **`C`**: Window bitsize, a key parameter in the "bucket method" for MSM. +- **`Bitsize`**: Number of bits of the largest scalar. +- **`BatchSize`**: Number of results to compute in one batch. +- **`ArePointsSharedInBatch`**: Bases are shared for batch. Set to true if all MSMs use the same bases. Otherwise, the number of bases and number of scalars are expected to be equal. +- **`areScalarsOnDevice`**: Indicates if scalars are located on the device. +- **`AreScalarsMontgomeryForm`**: True if scalars are in Montgomery form. +- **`areBasesOnDevice`**: Indicates if bases are located on the device. +- **`AreBasesMontgomeryForm`**: True if point coordinates are in Montgomery form. +- **`areResultsOnDevice`**: Indicates if results are stored on the device. +- **`IsAsync`**: If true, runs MSM asynchronously. +- **`Ext`**: Extended configuration for backend. + +### Default Configuration + +Use `GetDefaultMSMConfig` to obtain a default configuration, which can then be customized as needed. + +```go +func GetDefaultMSMConfig() MSMConfig +``` + +## Batched msm + +For batch msm, simply allocate the results array with size corresponding to batch size and set the `ArePointsSharedInBatch` flag in config struct. + +```go +... + +// Obtain the default MSM configuration. +cfg := GetDefaultMSMConfig() + +cfg.Ctx.IsBigTriangle = true + +... +``` + +## How do I toggle between MSM modes? + +Toggling between MSM modes occurs automatically based on the number of results you are expecting from the `MSM` function. + +The number of results is interpreted from the size of `var out core.DeviceSlice`. Thus its important when allocating memory for `var out core.DeviceSlice` to make sure that you are allocating ` X `. + +```go +... + +batchSize := 3 +var p G2Projective +var out core.DeviceSlice +out.Malloc(p.Size(), batchSize) + +... +``` + +## Parameters for optimal performance + +Please refer to the [primitive description](../primitives/msm#choosing-optimal-parameters) + +## Support for G2 group + +To activate G2 support first you must make sure you are building the static libraries with G2 feature enabled as described in the [Golang building instructions](../golang-bindings.md#using-icicle-golang-bindings-in-your-project). + +Now you may import `g2` package of the specified curve. + +```go +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/g2" +) +``` + +This package include `G2Projective` and `G2Affine` points as well as a `G2Msm` method. + +```go +package main + +import ( + "log" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + cfg := core.GetDefaultMSMConfig() + points := bn254.GenerateAffinePoints(1024) + var precomputeFactor int32 = 8 + var precomputeOut core.DeviceSlice + precomputeOut.Malloc(points[0].Size(), points.Len()*int(precomputeFactor)) + + err := msm.PrecomputeBases(points, &cfg, precomputeOut) + if err != runtime.Success { + log.Fatalf("PrecomputeBases failed: %v", err) + } +} +``` + +`G2Msm` works the same way as normal MSM, the difference is that it uses G2 Points. diff --git a/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/multi-gpu.md b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/multi-gpu.md new file mode 100644 index 000000000..7f0a66257 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/multi-gpu.md @@ -0,0 +1,161 @@ +# Multi GPU APIs + +To learn more about the theory of Multi GPU programming refer to [this part](../multi-device.md) of documentation. + +Here we will cover the core multi GPU apis and an [example](#a-multi-gpu-example) + +## A Multi GPU example + +In this example we will display how you can + +1. Fetch the number of devices installed on a machine +2. For every GPU launch a thread and set an active device per thread. +3. Execute a MSM on each GPU + +```go +package main + +import ( + "fmt" + "sync" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + bn254 "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + + device := runtime.CreateDevice("CUDA", 0) + err := runtime.SetDevice(&device) + numDevices, _ := runtime.GetDeviceCount() + fmt.Println("There are ", numDevices, " devices available") + + if err != runtime.Success { + panic(err) + } + wg := sync.WaitGroup{} + + for i := 0; i < numDevices; i++ { + internalDevice := runtime.Device{DeviceType: device.DeviceType, Id: int32(i)} + wg.Add(1) + runtime.RunOnDevice(&internalDevice, func(args ...any) { + defer wg.Done() + currentDevice, err := runtime.GetActiveDevice() + if err != runtime.Success { + panic("Failed to get current device") + } + + fmt.Println("Running on ", currentDevice.GetDeviceType(), " ", currentDevice.Id, " device") + + cfg := msm.GetDefaultMSMConfig() + cfg.IsAsync = true + size := 1 << 10 + scalars := bn254.GenerateScalars(size) + points := bn254.GenerateAffinePoints(size) + + stream, _ := runtime.CreateStream() + var p bn254.Projective + var out core.DeviceSlice + _, err = out.MallocAsync(p.Size(), 1, stream) + if err != runtime.Success { + panic("Allocating bytes on device for Projective results failed") + } + cfg.StreamHandle = stream + + err = msm.Msm(scalars, points, &cfg, out) + if err != runtime.Success { + panic("Msm failed") + } + outHost := make(core.HostSlice[bn254.Projective], 1) + outHost.CopyFromDeviceAsync(&out, stream) + out.FreeAsync(stream) + + runtime.SynchronizeStream(stream) + runtime.DestroyStream(stream) + // Check with gnark-crypto + }) + } + wg.Wait() +} +``` + +This example demonstrates a basic pattern for distributing tasks across multiple GPUs. The `RunOnDevice` function ensures that each goroutine is executed on its designated GPU and a corresponding thread. + +## Device Management API + +To streamline device management we offer as part of `runtime` package methods for dealing with devices. + +### `RunOnDevice` + +Runs a given function on a specific GPU device, ensuring that all CUDA calls within the function are executed on the selected device. + +In Go, most concurrency can be done via Goroutines. However, there is no guarantee that a goroutine stays on a specific host thread. + +`RunOnDevice` was designed to solve this caveat and ensure that the goroutine will stay on a specific host thread. + +`RunOnDevice` locks a goroutine into a specific host thread, sets a current GPU device, runs a provided function, and unlocks the goroutine from the host thread after the provided function finishes. + +While the goroutine is locked to the host thread, the Go runtime will not assign other goroutines to that host thread. + +**Parameters:** + +- **`device *Device`**: A pointer to the `Device` instance to be used to run code. +- **`funcToRun func(args ...any)`**: The function to be executed on the specified device. +- **`args ...any`**: Arguments to be passed to `funcToRun`. + +**Behavior:** + +- The function `funcToRun` is executed in a new goroutine that is locked to a specific OS thread to ensure that all CUDA calls within the function target the specified device. + +:::note +Any goroutines launched within `funcToRun` are not automatically bound to the same GPU device. If necessary, `RunOnDevice` should be called again within such goroutines with the same `deviceId`. +::: + +**Example:** + +```go +device := runtime.CreateDevice("CUDA", 0) +RunOnDevice(&device, func(args ...any) { + fmt.Println("This runs on GPU 0") + // CUDA-related operations here will target GPU 0 +}, nil) +``` + +### `SetDevice` + +Sets the active device for the current host thread. All subsequent calls made from this thread will target the specified device. + +:::warning +This function should not be used directly in conjunction with goroutines. If you want to run multi-gpu scenarios with goroutines you should use [RunOnDevice](#runondevice) +::: + +**Parameters:** + +- **`device *Device`**: A pointer to the `Device` instance to be used to run code. + +**Returns:** + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. + +### `GetDeviceCount` + +Retrieves the number of devices available on the host. + +**Returns:** + +- **`(int, EIcicleError)`**: The number of devices and an error code indicating the success or failure of the operation. + +### `GetActiveDevice` + +Gets the device of the currently active device for the calling host thread. + +**Returns:** + +- **`(*Device, EIcicleError)`**: The device pointer and an error code indicating the success or failure of the operation. + + +This documentation should provide a clear understanding of how to effectively manage multiple GPUs in Go applications using CUDA and other backends, with a particular emphasis on the `RunOnDevice` function for executing tasks on specific GPUs. diff --git a/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/ntt.md b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/ntt.md new file mode 100644 index 000000000..9a947603e --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/ntt.md @@ -0,0 +1,140 @@ +# NTT + +## NTT Example + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/ntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + + "github.com/consensys/gnark-crypto/ecc/bn254/fr/fft" +) + +func init() { + // Load backend using env path + runtime.LoadBackendFromEnvOrDefault() + // Set Cuda device to perform + device := runtime.CreateDevice("CUDA", 0) + runtime.SetDevice(&device) + + cfg := core.GetDefaultNTTInitDomainConfig() + initDomain(18, cfg) +} + +func initDomain(largestTestSize int, cfg core.NTTInitDomainConfig) runtime.EIcicleError { + rouMont, _ := fft.Generator(uint64(1 << largestTestSize)) + rou := rouMont.Bits() + rouIcicle := bn254.ScalarField{} + limbs := core.ConvertUint64ArrToUint32Arr(rou[:]) + + rouIcicle.FromLimbs(limbs) + e := ntt.InitDomain(rouIcicle, cfg) + return e +} + +func main() { + // Obtain the default NTT configuration with a predefined coset generator. + cfg := ntt.GetDefaultNttConfig() + + // Define the size of the input scalars. + size := 1 << 18 + + // Generate scalars for the NTT operation. + scalars := bn254.GenerateScalars(size) + + // Set the direction of the NTT (forward or inverse). + dir := core.KForward + + // Allocate memory for the results of the NTT operation. + results := make(core.HostSlice[bn254.ScalarField], size) + + // Perform the NTT operation. + err := ntt.Ntt(scalars, dir, &cfg, results) + if err != runtime.Success { + panic("NTT operation failed") + } + + ntt.ReleaseDomain() +} +``` + +## NTT Method + +```go +func Ntt[T any](scalars core.HostOrDeviceSlice, dir core.NTTDir, cfg *core.NTTConfig[T], results core.HostOrDeviceSlice) runtime.EIcicleError +``` + +### Parameters + +- **`scalars`**: A slice containing the input scalars for the transform. It can reside either in host memory or device memory. +- **`dir`**: The direction of the NTT operation (`KForward` or `KInverse`). +- **`cfg`**: A pointer to an `NTTConfig` object, containing configuration options for the NTT operation. +- **`results`**: A slice where the results of the NTT operation will be stored. This slice can be in host or device memory. + +### Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. + +## NTT Configuration (NTTConfig) + +The `NTTConfig` structure holds configuration parameters for the NTT operation, allowing customization of its behavior to optimize performance based on the specifics of your protocol. + +```go +type NTTConfig[T any] struct { + StreamHandle runtime.Stream + CosetGen T + BatchSize int32 + ColumnsBatch bool + Ordering Ordering + areInputsOnDevice bool + areOutputsOnDevice bool + IsAsync bool + Ext config_extension.ConfigExtensionHandler +} +``` + +### Fields + +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`CosetGen`**: Coset generator. Used to perform coset (i)NTTs. +- **`BatchSize`**: The number of NTTs to compute in one operation, defaulting to 1. +- **`ColumnsBatch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. +- **`Ordering`**: Ordering of inputs and outputs (`KNN`, `KNR`, `KRN`, `KRR`), affecting how data is arranged. +- **`areInputsOnDevice`**: Indicates if input scalars are located on the device. +- **`areOutputsOnDevice`**: Indicates if results are stored on the device. +- **`IsAsync`**: Controls whether the NTT operation runs asynchronously. +- **`Ext`**: Extended configuration for backend. + +### Default Configuration + +Use `GetDefaultNTTConfig` to obtain a default configuration, customizable as needed. + +```go +func GetDefaultNTTConfig[T any](cosetGen T) NTTConfig[T] +``` + +### Initializing the NTT Domain + +Before performing NTT operations, it's necessary to initialize the NTT domain; it only needs to be called once per GPU since the twiddles are cached. + +```go +func InitDomain(primitiveRoot bn254.ScalarField, cfg core.NTTInitDomainConfig) runtime.EIcicleError +``` + +This function initializes the domain with a given primitive root, optionally using fast twiddle factors to optimize the computation. + +### Releasing the domain + +The `ReleaseDomain` function is responsible for releasing the resources associated with a specific domain in the CUDA device context. + +```go +func ReleaseDomain() runtime.EIcicleError +``` + +### Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. diff --git a/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/vec-ops.md b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/vec-ops.md new file mode 100644 index 000000000..e219ec26d --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/golang-bindings/vec-ops.md @@ -0,0 +1,197 @@ +# Vector Operations + +## Overview + +Icicle exposes a number of vector operations which a user can use: + +* The VecOps API provides efficient vector operations such as addition, subtraction, and multiplication, supporting both single and batched operations. +* MatrixTranspose API allows a user to perform a transpose on a vector representation of a matrix, with support for batched transpositions. + +## VecOps API Documentation + +### Example + +#### Vector addition + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/vecOps" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + testSize := 1 << 12 + a := bn254.GenerateScalars(testSize) + b := bn254.GenerateScalars(testSize) + out := make(core.HostSlice[bn254.ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector multiplication + err := vecOps.VecOp(a, b, out, cfg, core.Add) + if err != runtime.Success { + panic("Vector addition failed") + } +} +``` + +#### Vector Subtraction + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/vecOps" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + testSize := 1 << 12 + a := bn254.GenerateScalars(testSize) + b := bn254.GenerateScalars(testSize) + out := make(core.HostSlice[bn254.ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector multiplication + err := vecOps.VecOp(a, b, out, cfg, core.Sub) + if err != runtime.Success { + panic("Vector subtraction failed") + } +} +``` + +#### Vector Multiplication + +```go +package main + +import ( + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/vecOps" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" +) + +func main() { + testSize := 1 << 12 + a := bn254.GenerateScalars(testSize) + b := bn254.GenerateScalars(testSize) + out := make(core.HostSlice[bn254.ScalarField], testSize) + cfg := core.DefaultVecOpsConfig() + + // Perform vector multiplication + err := vecOps.VecOp(a, b, out, cfg, core.Mul) + if err != runtime.Success { + panic("Vector multiplication failed") + } +} +``` + +### VecOps Method + +```go +func VecOp(a, b, out core.HostOrDeviceSlice, config core.VecOpsConfig, op core.VecOps) (ret runtime.EIcicleError) +``` + +#### Parameters + +- **`a`**: The first input vector. +- **`b`**: The second input vector. +- **`out`**: The output vector where the result of the operation will be stored. +- **`config`**: A `VecOpsConfig` object containing various configuration options for the vector operations. +- **`op`**: The operation to perform, specified as one of the constants (`Sub`, `Add`, `Mul`) from the `VecOps` type. + +#### Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. + +### VecOpsConfig + +The `VecOpsConfig` structure holds configuration parameters for the vector operations, allowing customization of its behavior. + +```go +type VecOpsConfig struct { + StreamHandle runtime.Stream + isAOnDevice bool + isBOnDevice bool + isResultOnDevice bool + IsAsync bool + batch_size int + columns_batch bool + Ext config_extension.ConfigExtensionHandler +} +``` + +#### Fields + +- **`StreamHandle`**: Specifies the stream (queue) to use for async execution. +- **`isAOnDevice`**: Indicates if vector `a` is located on the device. +- **`isBOnDevice`**: Indicates if vector `b` is located on the device. +- **`isResultOnDevice`**: Specifies where the result vector should be stored (device or host memory). +- **`IsAsync`**: Controls whether the vector operation runs asynchronously. +- **`batch_size`**: Number of vectors (or operations) to process in a batch. Each vector operation will be performed independently on each batch element. +- **`columns_batch`**: true if the batched vectors are stored as columns in a 2D array (i.e., the vectors are strided in memory as columns of a matrix). If false, the batched vectors are stored contiguously in memory (e.g., as rows or in a flat array). +- **`Ext`**: Extended configuration for backend. + +#### Default Configuration + +Use `DefaultVecOpsConfig` to obtain a default configuration, customizable as needed. + +```go +func DefaultVecOpsConfig() VecOpsConfig +``` + +## MatrixTranspose API Documentation + +This section describes the functionality of the `TransposeMatrix` function used for matrix transposition. + +The function takes a matrix represented as a 1D slice and transposes it, storing the result in another 1D slice. + +If VecOpsConfig specifies a batch_size greater than one, the transposition is performed on multiple matrices simultaneously, producing corresponding transposed matrices. The storage arrangement of batched matrices is determined by the columns_batch field in the VecOpsConfig. + +### Function + +```go +func TransposeMatrix(in, out core.HostOrDeviceSlice, columnSize, rowSize int, config core.VecOpsConfig) runtime.EIcicleError +``` + +## Parameters + +- **`in`**: The input matrix is a `core.HostOrDeviceSlice`, stored as a 1D slice. +- **`out`**: The output matrix is a `core.HostOrDeviceSlice`, which will be the transpose of the input matrix, stored as a 1D slice. +- **`columnSize`**: The number of columns in the input matrix. +- **`rowSize`**: The number of rows in the input matrix. +- **`config`**: A `VecOpsConfig` object containing various configuration options for the vector operations. + +## Return Value + +- **`EIcicleError`**: A `runtime.EIcicleError` value, which will be `runtime.Success` if the operation was successful, or an error if something went wrong. + +## Example Usage + +```go +var input = make(core.HostSlice[ScalarField], 20) +var output = make(core.HostSlice[ScalarField], 20) + +// Populate the input matrix +// ... + +// Get device context +cfg, _ := runtime.GetDefaultDeviceContext() + +// Transpose the matrix +err := TransposeMatrix(input, output, 5, 4, cfg) +if err != runtime.Success { + // Handle the error +} + +// Use the transposed matrix +// ... +``` + +In this example, the `TransposeMatrix` function is used to transpose a 5x4 matrix stored in a 1D slice. The input and output slices are stored on the host (CPU), and the operation is executed synchronously. diff --git a/docs/versioned_docs/version-3.1.0/icicle/install_cuda_backend.md b/docs/versioned_docs/version-3.1.0/icicle/install_cuda_backend.md new file mode 100644 index 000000000..f47dec7f0 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/install_cuda_backend.md @@ -0,0 +1,30 @@ + +# CUDA Backend + +## Overview + +The CUDA backend in ICICLE v3 is a high-performance, closed-source component designed to accelerate cryptographic computations using NVIDIA GPUs. This backend includes specialized libraries optimized for various cryptographic fields and curves, providing significant speedups for operations such as MSM, NTT, and elliptic curve operations. + +## Installation + +The CUDA backend is a closed-source component that requires a license. [To install the CUDA backend, see here](./getting_started#installing-and-using-icicle). + +### Licensing + +:::note +Currently, the CUDA backend is free to use via Ingonyama’s backend license server. By default, the CUDA backend will attempt to access this server. For more details, please contact support@ingonyama.com. +::: + +The CUDA backend requires a valid license to function. There are two types of CUDA backend licenses: + + 1. **Floating license**: In this mode, you host a license server, provided as a binary. This license supports a limited number of concurrent GPUs (N), which can be distributed across your machines as needed. N is decremented by 1 for each GPU using ICICLE per process. Once the process terminates (or crashes), the licenses are released. + 2. **Node locked license**: In this mode, the license is tied to a specific machine. The CUDA backend will accept the license only if it is used on the licensed machine. + +**To specify the license server address or file path::** + +``` +export ICICLE_LICENSE=port@ip # For license server +export ICICLE_LICENSE=/path/to/license # For node-locked license +``` + +For further assist , contact our support team for assistance support@ingonyama.com diff --git a/docs/versioned_docs/version-3.1.0/icicle/integrations.md b/docs/versioned_docs/version-3.1.0/icicle/integrations.md new file mode 100644 index 000000000..3a9e29cd5 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/integrations.md @@ -0,0 +1,97 @@ +# ICICLE integrated provers + +ICICLE has been used by companies and projects such as [Celer Network](https://github.com/celer-network), [Consensys Gnark](https://github.com/Consensys/gnark), [EZKL](https://blog.ezkl.xyz/post/acceleration/), [ZKWASM](https://twitter.com/DelphinusLab/status/1762604988797513915) and others to accelerate their ZK proving pipeline. + +Many of these integrations have been a collaboration between Ingonyama and the integrating company. We have learned a lot about designing GPU based ZK provers. + +If you're interested in understanding these integrations better or learning how you can use ICICLE to accelerate your existing ZK proving pipeline this is the place for you. + +## A primer to building your own integrations + +Lets illustrate an ICICLE integration, so you can understand the core API and design overview of ICICLE. + +![ICICLE architecture](/img/architecture-high-level.png) + +Engineers usually use a cryptographic library to implement their ZK protocols. These libraries implement efficient primitives which are used as building blocks for the protocol; ICICLE is such a library. The difference is that ICICLE is designed from the start to run on GPUs; the Rust and Golang APIs abstract away all low level CUDA details. Our goal was to allow developers with no GPU experience to quickly get started with ICICLE. + +A developer may use ICICLE with two main approaches in mind. + +1. Drop-in replacement approach. +2. End-to-End GPU replacement approach. + +The first approach for GPU-accelerating your Prover with ICICLE is quick to implement, but it has limitations, such as reduced memory optimization and limited protocol tuning for GPUs. It's a solid starting point, but those committed to fully leveraging GPU acceleration should consider a more comprehensive approach. + +A End-to-End GPU replacement means performing the entire ZK proof on the GPU. This approach will reduce latency to a minimum and requires you to change the way you implement the protocol to be more GPU friendly. This approach will take full advantage of GPU acceleration. Redesigning your prover this way may take more engineering effort but we promise you that its worth it! + +## Using ICICLE integrated provers + +Here we cover how a developer can run existing circuits on ICICLE integrated provers. + +### Gnark + +[Gnark](https://github.com/Consensys/gnark) officially supports GPU proving with ICICLE. Currently only Groth16 on curve `BN254` is supported. This means that if you are currently using Gnark to write your circuits you can enjoy GPU acceleration without making many changes. + +:::info + +Currently ICICLE has been merged to Gnark [master branch](https://github.com/Consensys/gnark), however the [latest release](https://github.com/Consensys/gnark/releases/tag/v0.9.1) is from October 2023. + +::: + +Make sure your golang circuit project has `gnark` as a dependency and that you are using the master branch for now. + +``` +go get github.com/consensys/gnark@master +``` + +You should see two indirect dependencies added. + +``` +... + github.com/ingonyama-zk/icicle v0.1.0 // indirect + github.com/ingonyama-zk/iciclegnark v0.1.1 // indirect +... +``` + +:::info +As you may notice we are using ICICLE v0.1 here since golang bindings are only support in ICICLE v0.1 for the time being. +::: + +To switch over to ICICLE proving, make sure to change the backend you are using, below is an example of how this should be done. + +``` +// toggle on +proofIci, err := groth16.Prove(ccs, pk, secretWitness, backend.WithIcicleAcceleration()) + +// toggle off +proof, err := groth16.Prove(ccs, pk, secretWitness) +``` + +Now that you have enabled `WithIcicleAcceleration` backend simple change the way your run your circuits to: + +``` +go run -tags=icicle main.go +``` + +Your logs should look something like this if everything went as expected. + +``` +13:12:05 INF compiling circuit +13:12:05 INF parsed circuit inputs nbPublic=1 nbSecret=1 +13:12:05 INF building constraint builder nbConstraints=3 +13:12:05 DBG precomputing proving key in GPU acceleration=icicle backend=groth16 curve=bn254 nbConstraints=3 +13:12:05 DBG constraint system solver done nbConstraints=3 took=0.070259 +13:12:05 DBG prover done acceleration=icicle backend=groth16 curve=bn254 nbConstraints=3 took=80.356684 +13:12:05 DBG verifier done backend=groth16 curve=bn254 took=1.843888 +``` + +`acceleration=icicle` indicates that the prover is running in acceleration mode with ICICLE. + +You can reference the [Gnark docs](https://github.com/Consensys/gnark?tab=readme-ov-file#gpu-support) for further information. + +### Halo2 + +[Halo2](https://github.com/zkonduit/halo2) fork integrated with ICICLE for GPU acceleration. This means that you can run your existing Halo2 circuits with GPU acceleration just by activating a feature flag. + +To enable GPU acceleration just enable `icicle_gpu` [feature flag](https://github.com/zkonduit/halo2/blob/3d7b5e61b3052680ccb279e05bdcc21dd8a8fedf/halo2_proofs/Cargo.toml#L102). + +This feature flag will seamlessly toggle on GPU acceleration for you. diff --git a/docs/versioned_docs/version-3.1.0/icicle/libraries.md b/docs/versioned_docs/version-3.1.0/icicle/libraries.md new file mode 100644 index 000000000..eafcc5975 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/libraries.md @@ -0,0 +1,67 @@ +# ICICLE libraries + +ICICLE is composed of two main logical parts: +1. [**ICICLE device library**](#icicle-device) +2. [**ICICLE template core library**](#icicle-core) + +## ICICLE device + +The ICICLE device library serves as an abstraction layer for interacting with various hardware devices. It provides a comprehensive interface for tasks such as setting the active device, querying device-specific information like free and total memory, determining the number of available devices, and managing memory allocation. Additionally, it offers functionality for copying data to and from devices, managing task queues (streams) for efficient device utilization, and abstracting the complexities of device management away from the user. + +See programmers guide for more details. [C++](./programmers_guide/cpp#device-management), [Rust](./programmers_guide/rust#device-management), [Go](./programmers_guide/go) + +## ICICLE Core + +ICICLE Core is a template library written in C++ that implements fundamental cryptographic operations, including field and curve arithmetic, as well as higher-level APIs such as MSM and NTT. + +The Core can be [instantiated](./build_from_source) for different fields, curves, and other cryptographic components, allowing you to tailor it to your specific needs. You can link your application to one or more ICICLE libraries, depending on the requirements of your project. For example, you might only need the babybear library or a combination of babybear and a Merkle tree builder. + + +### Rust +Each library has a corresponding crate. See [programmers guide](./programmers_guide/general.md) for more details. + +### Supported curves, fields and operations + +#### Supported curves and operations + +| Operation\Curve | [bn254](https://neuromancer.sk/std/bn/bn254) | [bls12-377](https://neuromancer.sk/std/bls/BLS12-377) | [bls12-381](https://neuromancer.sk/std/bls/BLS12-381) | [bw6-761](https://eprint.iacr.org/2020/351) | grumpkin | +| ----------------------------------------- | :------------------------------------------: | :---------------------------------------------------: | :---------------------------------------------------: | :-----------------------------------------: | :------: | +| [MSM](./primitives/msm) | ✅ | ✅ | ✅ | ✅ | ✅ | +| G2 MSM | ✅ | ✅ | ✅ | ✅ | ❌ | +| [NTT](./primitives/ntt) | ✅ | ✅ | ✅ | ✅ | ❌ | +| ECNTT | ✅ | ✅ | ✅ | ✅ | ❌ | +| [Vector operations](./primitives/vec_ops) | ✅ | ✅ | ✅ | ✅ | ✅ | +| [Polynomials](./polynomials/overview) | ✅ | ✅ | ✅ | ✅ | ❌ | +| [Poseidon](primitives/hash#poseidon) | ✅ | ✅ | ✅ | ✅ | ✅ | + +#### Supported fields and operations + +| Operation\Field | [babybear](https://eprint.iacr.org/2023/824.pdf) | [Stark252](https://docs.starknet.io/documentation/architecture_and_concepts/Cryptography/p-value/) | m31 | +| ----------------------------------------- | :----------------------------------------------: | :------------------------------------------------------------------------------------------------: | :---: | +| [Vector operations](./primitives/vec_ops) | ✅ | ✅ | ✅ | +| [Polynomials](./polynomials/overview) | ✅ | ✅ | ❌ | +| [NTT](primitives/ntt) | ✅ | ✅ | ❌ | +| Extension Field | ✅ | ❌ | ✅ | +| [Poseidon](primitives/hash#poseidon) | ✅ | ✅ | ✅ | + +### Misc + +| Operation | Description | +| ----------------------------------------- | :------------------------------------------: | +| [Keccak](primitives/hash#keccak-and-sha3) | supporting 256b and 512b digest | +| [SHA3](primitives/hash#keccak-and-sha3) | supporting 256b and 512b digest | +| [Blake2s](primitives/hash#blake2s) | digest is 256b | +| [Merkle-Tree](primitives/merkle) | works with any combination of hash functions | + + + + + + +## Backend +Each backend must implement the device API interface. +Each backend may implement +- One or more ICICLE library. For example implement only bn254 curve. +- One or more APIs in this library. For example MSM only. + +See [CUDA backend](./install_cuda_backend.md) and [Build Your Own Backend](./build_your_own_backend.md) for more info about implementing a backend. diff --git a/docs/versioned_docs/version-3.1.0/icicle/migrate_from_v2.md b/docs/versioned_docs/version-3.1.0/icicle/migrate_from_v2.md new file mode 100644 index 000000000..acb2ff482 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/migrate_from_v2.md @@ -0,0 +1,93 @@ + +# Migration from ICICLE v2 to v3 + +ICICLE v3 introduces a unified interface for high-performance computing across various devices, extending the functionality that was previously limited to GPUs in Icicle V2. This guide will assist you in transitioning from ICICLE v2 to v3 by highlighting the key changes and providing examples for both C++ and Rust. + +## Key Conceptual Changes + +- **Device Independence**: n V2, Icicle was primarily designed for GPU computation, directly utilizing CUDA APIs. In V3, Icicle has evolved to support a broader range of computational devices, including CPUs, GPUs, and other accelerators. As a result, CUDA APIs have been replaced with device-agnostic Icicle APIs. + +- **Unified API**: The APIs are now standardized across all devices, ensuring consistent usage and reducing the complexity of managing different hardware backends. + +:::warning +When migrating from v2 to v3, it is important to note that, by default, your code now executes on the CPU. This contrasts with V2, which was exclusively a CUDA library. For details on installing and using CUDA GPUs, refer to the [CUDA backend guide](./install_cuda_backend.md). +::: + +## Migration Guide for C++ + +### Replacing CUDA APIs with Icicle APIs + +In ICICLE v3, CUDA-specific APIs have been replaced with Icicle APIs that are designed to be backend-agnostic. This allows your code to run on different devices without requiring modifications. + +- **Device Management**: Use ICICLE's device management APIs instead of CUDA-specific functions. For example, instead of `cudaSetDevice()`, you would use `icicle_set_device()`. + +- **Memory Management**: Replace CUDA memory management functions such as `cudaMalloc()` and `cudaFree()` with ICICLE's `icicle_malloc()` and `icicle_free()`. + +- **Stream Management**: Replace `cudaStream_t` with `icicleStreamHandle` and use ICICLE's stream management functions. + +For a detailed overview and examples, please refer to the [Icicle C++ Programmer's Guide](./programmers_guide/cpp.md) for full API details. + +### Example Migration + +**V2 (CUDA-specific):** +```cpp +cudaStream_t stream; +cudaStreamCreate(&stream); +void* device_ptr; +cudaMalloc(&device_ptr, 1024); +// Perform operations using CUDA APIs +cudaStreamDestroy(stream); +cudaFree(device_ptr); +``` + +**V3 (Device-agnostic):** +```cpp +icicleStreamHandle stream; +icicle_create_stream(&stream); +void* device_ptr; +icicle_malloc(&device_ptr, 1024); +// Perform operations using Icicle APIs +icicle_destroy_stream(stream); +icicle_free(device_ptr); +``` + +## Migration Guide for Rust + +### Replacing `icicle_cuda_runtime` with `icicle_runtime` + +In ICICLE v3, the `icicle_cuda_runtime` crate is replaced with the `icicle_runtime` crate. This change reflects the broader support for different devices beyond just CUDA-enabled GPUs. + +- **Device Management**: Use `icicle_runtime`'s device management functions instead of those in `icicle_cuda_runtime`. The `Device` struct remains central, but it's now part of a more generalized runtime. + +- **Memory Abstraction**: The `DeviceOrHostSlice` trait remains for memory abstraction, allowing seamless data handling between host and device. + +- **Stream Management**: Replace `CudaStream` with `IcicleStream`, which now supports broader device types. + +### Example Migration + +**V2 (`icicle_cuda_runtime`):** +```rust +use icicle_cuda_runtime::{CudaStream, DeviceVec, HostSlice}; + +let mut stream = CudaStream::create().unwrap(); +let mut device_memory = DeviceVec::cuda_malloc(1024).unwrap(); +// Perform operations using CudaStream and related APIs +stream.synchronize().unwrap(); +``` + +**V3 (`icicle_runtime`):** +```rust +use icicle_runtime::{IcicleStream, DeviceVec, HostSlice}; + +let mut stream = IcicleStream::create().unwrap(); +let mut device_memory = DeviceVec::device_malloc(1024).unwrap(); +// Perform operations using IcicleStream and related APIs +stream.synchronize().unwrap(); +``` + +### Other Considerations + +- **API Names**: While most API names remain consistent, they are now part of a more generalized runtime that can support multiple devices. Ensure that you update the crate imports and function calls accordingly. +- **Backend Loading**: Ensure that you are loading the appropriate backend using the `load_backend_from_env_or_default()` function, which is essential for setting up the runtime environment. + +For further details and examples, refer to the [Programmer's Guide](./programmers_guide/general.md). diff --git a/docs/versioned_docs/version-3.1.0/icicle/multi-device.md b/docs/versioned_docs/version-3.1.0/icicle/multi-device.md new file mode 100644 index 000000000..fd2957b81 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/multi-device.md @@ -0,0 +1,79 @@ + +# Multi-Device with ICICLE + +:::info +If you are looking for the Multi-Device API documentation refer [here](./rust-bindings/multi-gpu.md) for Rust and [here](./golang-bindings/multi-gpu.md) for Golang. +::: + +One common challenge with Zero-Knowledge (ZK) computation is managing large input sizes. It's not uncommon to encounter circuits surpassing 2^25 constraints, which can push even the most advanced compute devices to their limits. To effectively scale and process such large circuits, leveraging multiple devices becomes a necessity. + +Multi-Device programming involves developing software to operate across multiple GPUs, CPUs, or other specialized hardware devices. Let's first explore different approaches to Multi-Device programming, then we will cover how ICICLE allows you to easily develop your ZK computations to run across many devices. + +## Approaches to Multi-Device Programming + +There are many [different strategies](https://github.com/NVIDIA/multi-gpu-programming-models) available for implementing multi-device systems, but they can generally be split into two categories: + +### Device Server Approach + +This approach typically involves a single or multiple CPUs managing threads that read from and write to multiple devices, such as GPUs, CPUs, or accelerators. You can think of it as a scaled-up HOST-Device model. + +![Multi-Device Server Approach](/img/image.png) + +This approach doesn't necessarily allow for tackling larger computation sizes, but it does enable the simultaneous computation of tasks that wouldn't fit on a single device. + +For example, if you needed to compute two MSMs of size 2^26 on a GPU with 16GB of VRAM, you would normally have to perform these tasks asynchronously. However, by doubling the number of GPUs or other devices in your system, you can now run these computations in parallel. + +### Inter-Device Approach + +This more sophisticated approach involves leveraging technologies such as [GPUDirect, NCCL, NVSHMEM](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-cwes1084/), NVLink, or other interconnect technologies to combine multiple devices and split a computation across different hardware. + +This approach requires redesigning the algorithm at the software level to be compatible with splitting tasks among devices. In some cases, to minimize latency, special inter-device connections can be installed on a server to allow direct communication between multiple devices. + +## Writing ICICLE Code for Multi-Device Setups + +Currently, ICICLE adopts a Device Server approach, where we assume you have a machine with multiple devices (GPUs, CPUs, etc.) and wish to run computations on each device. + +Each thread needs to set a device. Following API calls (including memory management and compute APIs) will execute on that device, for this thread. + +### C++ +```cpp +// Set the device to CPU +Device device = "CPU"; +icicle_set_device(device); +``` +### Rust +```rust +// Set the device to the first CUDA GPU (GPU-0) +let device = icicle_runtime::Device::new("CUDA", 0 /* =device_id*/); +icicle_runtime::set_device(&device).unwrap(); +``` + +### Go (TODO) + +## Best Practices + +- **Avoid Hardcoding Device IDs:** If you want your software to take full advantage of all available devices on a machine, use methods such as `icicle_get_device_count()` to support an arbitrary number of devices. +```cpp +/** + * @brief Checks if the specified device is available. + * + * @param dev The device to check for availability. + * @return eIcicleError Status of the device availability check. + * - `SUCCESS` if the device is available. + * - `INVALID_DEVICE` if the device is not available. + */ +extern "C" eIcicleError icicle_is_device_available(const Device& dev); + + /** + * @brief Get number of available devices active device for thread + * + + * @return eIcicleError Status of the device set + */ +extern "C" eIcicleError icicle_get_device_count(int& device_count /*OUT*/); +``` + +- **Launch One CPU Thread per Device:** To avoid [common errors](https://developer.nvidia.com/blog/cuda-pro-tip-always-set-current-device-avoid-multithreading-bugs/) and ensure code readability, we recommend creating a dedicated thread for each device. Within each CPU thread, you can launch as many tasks as you like for a device, as long as they all run on the same device ID. This will make your code more manageable, easier to read, and performant. +--- + +This guide provides an overview of multi-device support in ICICLE, explaining the approaches and best practices for efficiently scaling ZK computations across multiple devices. For further information or support, please refer to our [documentation](../introduction.md) or join the discussion on [Discord](https://discord.gg/6vYrE7waPj). diff --git a/docs/versioned_docs/version-3.1.0/icicle/overview.md b/docs/versioned_docs/version-3.1.0/icicle/overview.md new file mode 100644 index 000000000..9201e13b5 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/overview.md @@ -0,0 +1,82 @@ +--- +slug: /icicle/overview +title: ICICLE Overview +--- + +# ICICLE Overview + +

+ Untitled design (29) +

+ +## What is ICICLE? + +[ICICLE](https://github.com/ingonyama-zk/icicle) is a cryptography library designed to accelerate zero-knowledge proofs (ZKPs) using multiple compute backends, including GPUs, CPUs, and potentially other platforms. ICICLE's key strength lies in its ability to implement blazing-fast cryptographic primitives, enabling developers to significantly reduce proving times with minimal effort. + +## Key Features + +- **Acceleration of “zk” Math:** ICICLE provides optimized implementations for cryptographic primitives crucial to zero-knowledge proofs, such as elliptic curve operations, MSM, NTT, Poseidon hash, and more. +- **Set of Libraries:** ICICLE includes a comprehensive set of libraries supporting various fields, curves, and other cryptographic needs. +- **Cross-Language Support:** Available bindings for C++, Rust, Go, and potentially Python make ICICLE accessible across different development environments. +- **Backend Agnosticism:** Develop on CPU and deploy on various backends, including GPUs, specialized hardware, and other emerging platforms, depending on your project's needs. +- **Extensibility:** Designed for easy integration and extension, allowing you to build and deploy custom backends and cryptographic primitives. + +## Evolution from v2 to v3 + +Originally, ICICLE was focused solely on GPU acceleration. With the release of v3, ICICLE now supports multiple backends, making it more versatile and adaptable to different hardware environments. Whether you're leveraging the power of GPUs or exploring other compute platforms, ICICLE v3 is designed to fit your needs. + +## Who Uses ICICLE? + +ICICLE has been successfully integrated and used by leading ZK companies such as [Celer Network](https://github.com/celer-network), [Gnark](https://github.com/Consensys/gnark), and others to enhance their ZK proving pipelines. + +## Don't Have Access to a GPU? + +We understand that not all developers have access to GPUs, but this shouldn't limit your ability to develop with ICICLE. Here are some ways to gain access to GPUs. + +### Grants + +At Ingonyama, we are committed to accelerating progress in ZK and cryptography. If you're an engineer, developer, or academic researcher, we invite you to check out [our grant program](https://www.ingonyama.com/blog/icicle-for-researchers-grants-challenges). We can provide access to GPUs and even fund your research. + +### Google Colab + +Google Colab is a great platform to start experimenting with ICICLE instantly. It offers free access to NVIDIA T4 GPUs, which are more than sufficient for experimenting and prototyping with ICICLE. + +For a detailed guide on setting up Google Colab with ICICLE, refer to [this article](./colab-instructions.md). + +### Vast.ai + +[Vast.ai](https://vast.ai/) offers a global GPU marketplace where you can rent different types of GPUs by the hour at competitive prices. Whether you need on-demand or interruptible rentals, Vast.ai provides flexibility for various use cases. Learn more about their rental options [here](https://vast.ai/faq#rental-types). + +## What Can You Do with ICICLE? + +[ICICLE](https://github.com/ingonyama-zk/icicle) can be used similarly to any other cryptography library. Through various integrations, ICICLE has proven effective in multiple use cases: + +### Boost Your ZK Prover Performance + +If you're a circuit developer facing bottlenecks, integrating ICICLE into your prover may solve performance issues. ICICLE is integrated into popular ZK provers like [Gnark](https://github.com/Consensys/gnark) and [Halo2](https://github.com/zkonduit/halo2), enabling immediate GPU acceleration without additional code changes. + +### Integrating into Existing ZK Provers + +ICICLE allows for selective acceleration of specific parts of your ZK prover, helping to address specific bottlenecks without requiring a complete rewrite of your prover. + +### Developing Your Own ZK Provers + +For those building ZK provers from the ground up, ICICLE is an ideal tool for creating optimized and scalable provers. The ability to scale across multiple machines within a data center is a key advantage when using ICICLE with GPUs. + +### Developing Proof of Concepts + +ICICLE is also well-suited for prototyping and developing small-scale projects. With bindings for Golang and Rust, you can easily create a library implementing specific cryptographic primitives, such as a KZG commitment library, using ICICLE. + +--- + +## Get Started with ICICLE + +Explore the full capabilities of ICICLE by diving into the [Architecture](./arch_overview.md), [Getting Started Guide](./getting_started.md) and the [Programmer's Guide](./programmers_guide/general.md) to learn how to integrate, deploy, and extend ICICLE across different backends. + +If you have any questions or need support, feel free to reach out on [Discord], [GitHub] or [via support email][SupportEmail]. We're here to help you accelerate your ZK development with ICICLE. + + +[Discord]: https://discord.gg/6vYrE7waPj +[Github]: https://github.com/ingonyama-zk +[SupportEmail]: mailto:support@ingonyama.com + diff --git a/docs/versioned_docs/version-3.1.0/icicle/polynomials/ffi.uml b/docs/versioned_docs/version-3.1.0/icicle/polynomials/ffi.uml new file mode 100644 index 000000000..5d0a1e3c8 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/polynomials/ffi.uml @@ -0,0 +1,27 @@ +@startuml +skinparam componentStyle uml2 + +' Define Components +component "C++ Template\nComponent" as CppTemplate { + [Parameterizable Interface] +} +component "C API Wrapper\nComponent" as CApiWrapper { + [C API Interface] +} +component "Rust Code\nComponent" as RustCode { + [Macro Interface\n(Template Instantiation)] +} + +' Define Artifact +artifact "Static Library\n«artifact»" as StaticLib + +' Connections +CppTemplate -down-> CApiWrapper : Instantiates +CApiWrapper .down.> StaticLib : Compiles into +RustCode -left-> StaticLib : Links against\nand calls via FFI + +' Notes +note right of CppTemplate : Generic C++\ntemplate implementation +note right of CApiWrapper : Exposes C API for FFI\nto Rust/Go +note right of RustCode : Uses macros to\ninstantiate templates +@enduml diff --git a/docs/versioned_docs/version-3.1.0/icicle/polynomials/hw_backends.uml b/docs/versioned_docs/version-3.1.0/icicle/polynomials/hw_backends.uml new file mode 100644 index 000000000..bb96db092 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/polynomials/hw_backends.uml @@ -0,0 +1,86 @@ +@startuml + +' Define Interface for Polynomial Backend Operations +interface IPolynomialBackend { + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +' Define Interface for Polynomial Context (State Management) +interface IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +' PolynomialAPI now uses two strategies: Backend and Context +class PolynomialAPI { + -backendStrategy: IPolynomialBackend + -contextStrategy: IPolynomialContext + -setBackendStrategy(IPolynomialBackend) + -setContextStrategy(IPolynomialContext) + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +' Backend Implementations +class GPUPolynomialBackend implements IPolynomialBackend { + #gpuResources: Resource + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +class ZPUPolynomialBackend implements IPolynomialBackend { + #zpuResources: Resource + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +class TracerPolynomialBackend implements IPolynomialBackend { + #traceData: Data + +add() + +subtract() + +multiply() + +divide() + +evaluate() +} + +' Context Implementations (Placeholder for actual implementation) +class GPUContext implements IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +class ZPUContext implements IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +class TracerContext implements IPolynomialContext { + +initFromCoeffs() + +initFromEvals() + +getCoeffs() + +getEvals() +} + +' Relationships +PolynomialAPI o-- IPolynomialBackend : uses +PolynomialAPI o-- IPolynomialContext : uses +@enduml diff --git a/docs/versioned_docs/version-3.1.0/icicle/polynomials/overview.md b/docs/versioned_docs/version-3.1.0/icicle/polynomials/overview.md new file mode 100644 index 000000000..881eeef88 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/polynomials/overview.md @@ -0,0 +1,376 @@ +# Polynomial API Overview + +:::note +Read our paper on the Polynomials API in ICICLE v2 by clicking [here](https://eprint.iacr.org/2024/973). +::: + +## Introduction + +The Polynomial API offers a robust framework for polynomial operations within a computational environment. It's designed for flexibility and efficiency, supporting a broad range of operations like arithmetic, evaluation, and manipulation, all while abstracting from the computation and storage specifics. This enables adaptability to various backend technologies, employing modern C++ practices. + +## Key Features + +### Backend Agnostic Architecture + +Our API is structured to be independent of any specific computational backend. While a CUDA backend is currently implemented, the architecture facilitates easy integration of additional backends. This capability allows users to perform polynomial operations without the need to tailor their code to specific hardware, enhancing code portability and scalability. + +### Templating in the Polynomial API + +The Polynomial API is designed with a templated structure to accommodate different data types for coefficients, the domain, and images. This flexibility allows the API to be adapted for various computational needs and types of data. + +```cpp +template +class Polynomial { + // Polynomial class definition +} +``` + +In this template: + +- **`Coeff`**: Represents the type of the coefficients of the polynomial. +- **`Domain`**: Specifies the type for the input values over which the polynomial is evaluated. By default, it is the same as the type of the coefficients but can be specified separately to accommodate different computational contexts. +- **`Image`**: Defines the type of the output values of the polynomial. This is typically the same as the coefficients. + +#### Default instantiation + +```cpp +extern template class Polynomial; +``` + +#### Extended use cases + +The templated nature of the Polynomial API also supports more complex scenarios. For example, coefficients and images could be points on an elliptic curve (EC points), which are useful in cryptographic applications and advanced algebraic structures. This approach allows the API to be extended easily to support new algebraic constructions without modifying the core implementation. + +### Supported Operations + +The Polynomial class encapsulates a polynomial, providing a variety of operations: + +- **Construction**: Create polynomials from coefficients or evaluations on roots-of-unity domains. +- **Arithmetic Operations**: Perform addition, subtraction, multiplication, and division. +- **Evaluation**: Directly evaluate polynomials at specific points or across a domain. +- **Manipulation**: Features like slicing polynomials, adding or subtracting monomials inplace, and computing polynomial degrees. +- **Memory Access**: Access internal states or obtain device-memory views of polynomials. + +## Usage + +This section outlines how to use the Polynomial API in C++. Bindings for Rust and Go are detailed under the Bindings sections. + +:::note +Make sure to set an ICICLE device prior to using the polynomial API. +::: + +### Construction + +Polynomials can be constructed from coefficients, from evaluations on roots-of-unity domains, or by cloning existing polynomials. + +```cpp +// Construction +static Polynomial from_coefficients(const Coeff* coefficients, uint64_t nof_coefficients); +static Polynomial from_rou_evaluations(const Image* evaluations, uint64_t nof_evaluations); +// Clone the polynomial +Polynomial clone() const; +``` + +Example: + +```cpp +auto p_from_coeffs = Polynomial_t::from_coefficients(coeff /* :scalar_t* */, nof_coeffs); +auto p_from_rou_evals = Polynomial_t::from_rou_evaluations(rou_evals /* :scalar_t* */, nof_evals); +auto p_cloned = p.clone(); // p_cloned and p do not share memory +``` + +:::note +The coefficients or evaluations may be allocated either on host or device memory. In both cases the memory is copied to the backend device. +::: + +### Arithmetic + +Constructed polynomials can be used for various arithmetic operations: + +```cpp +// Addition +Polynomial operator+(const Polynomial& rhs) const; +Polynomial& operator+=(const Polynomial& rhs); // inplace addition + +// Subtraction +Polynomial operator-(const Polynomial& rhs) const; + +// Multiplication +Polynomial operator*(const Polynomial& rhs) const; +Polynomial operator*(const Domain& scalar) const; // scalar multiplication + +// Division A(x) = B(x)Q(x) + R(x) +std::pair divide(const Polynomial& rhs) const; // returns (Q(x), R(x)) +Polynomial operator/(const Polynomial& rhs) const; // returns quotient Q(x) +Polynomial operator%(const Polynomial& rhs) const; // returns remainder R(x) +Polynomial divide_by_vanishing_polynomial(uint64_t degree) const; // sdivision by the vanishing polynomial V(x)=X^N-1 +``` + +#### Example + +Given polynomials A(x),B(x),C(x) and V(x) the vanishing polynomial. + +$$ +H(x)=\frac{A(x) \cdot B(x) - C(x)}{V(x)} \space where \space V(x) = X^{N}-1 +$$ + +```cpp +auto H = (A*B-C).divide_by_vanishing_polynomial(N); +``` + +### Evaluation + +Evaluate polynomials at arbitrary domain points, across a domain or on a roots-of-unity domain. + +```cpp +Image operator()(const Domain& x) const; // evaluate f(x) +void evaluate(const Domain* x, Image* evals /*OUT*/) const; +void evaluate_on_domain(Domain* domain, uint64_t size, Image* evals /*OUT*/) const; // caller allocates memory +void evaluate_on_rou_domain(uint64_t domain_log_size, Image* evals /*OUT*/) const; // caller allocate memory +``` + +Example: + +```cpp +Coeff x = rand(); +Image f_x = f(x); // evaluate f at x + +// evaluate f(x) on a domain +uint64_t domain_size = ...; +auto domain = /*build domain*/; // host or device memory +auto evaluations = std::make_unique(domain_size); // can be device memory too +f.evaluate_on_domain(domain, domain_size, evaluations); + +// evaluate f(x) on roots of unity domain +uint64_t domain_log_size = ...; +auto evaluations_rou_domain = std::make_unique(1 << domain_log_size); // can be device memory too +f.evaluate_on_rou_domain(domain_log_size, evaluations_rou_domain); +``` + +### Manipulations + +Beyond arithmetic, the API supports efficient polynomial manipulations: + +#### Monomials + +```cpp +// Monomial operations +Polynomial& add_monomial_inplace(Coeff monomial_coeff, uint64_t monomial = 0); +Polynomial& sub_monomial_inplace(Coeff monomial_coeff, uint64_t monomial = 0); +``` + +The ability to add or subtract monomials directly and in-place is an efficient way to manipualte polynomials. + +Example: + +```cpp +f.add_monomial_in_place(scalar_t::from(5)); // f(x) += 5 +f.sub_monomial_in_place(scalar_t::from(3), 8); // f(x) -= 3x^8 +``` + +#### Computing the degree of a Polynomial + +```cpp +// Degree computation +int64_t degree(); +``` + +The degree of a polynomial is a fundamental characteristic that describes the highest power of the variable in the polynomial expression with a non-zero coefficient. +The `degree()` function in the API returns the degree of the polynomial, corresponding to the highest exponent with a non-zero coefficient. + +- For the polynomial $f(x) = x^5 + 2x^3 + 4$, the degree is 5 because the highest power of $x$ with a non-zero coefficient is 5. +- For a scalar value such as a constant term (e.g., $f(x) = 7$, the degree is considered 0, as it corresponds to $x^0$. +- The degree of the zero polynomial, $f(x) = 0$, where there are no non-zero coefficients, is defined as -1. This special case often represents an "empty" or undefined state in many mathematical contexts. + +Example: + +```cpp +auto f = /*some expression*/; +auto degree_of_f = f.degree(); +``` + +#### Slicing + +```cpp +// Slicing and selecting even or odd components. +Polynomial slice(uint64_t offset, uint64_t stride, uint64_t size = 0 /*0 means take all elements*/); +Polynomial even(); +Polynomial odd(); +``` + +The Polynomial API provides methods for slicing polynomials and selecting specific components, such as even or odd indexed terms. Slicing allows extracting specific sections of a polynomial based on an offset, stride, and size. + +The following examples demonstrate folding a polynomial's even and odd parts and arbitrary slicing; + +```cpp +// folding a polynomials even and odd parts with randomness +auto x = rand(); +auto even = f.even(); +auto odd = f.odd(); +auto fold_poly = even + odd * x; + +// arbitrary slicing (first quarter) +auto first_quarter = f.slice(0 /*offset*/, 1 /*stride*/, f.degree()/4 /*size*/); +``` + +### Memory access (copy/view) + +Access to the polynomial's internal state can be vital for operations like commitment schemes or when more efficient custom operations are necessary. This can be done either by copying or viewing the polynomial + +#### Copying + +Copies the polynomial coefficients to either host or device allocated memory. + +:::note +Copying to host memory is backend agnostic while copying to device memory requires the memory to be allocated on the corresponding backend. +::: + +```cpp +Coeff get_coeff(uint64_t idx) const; // copy single coefficient to host +uint64_t copy_coeffs(Coeff* coeffs, uint64_t start_idx, uint64_t end_idx) const; +``` + +Example: + +```cpp +auto coeffs_device = /*allocate CUDA or host memory*/ +f.copy_coeffs(coeffs_device, 0/*start*/, f.degree()); + +MSMConfig cfg = msm::defaultMSMConfig(); +cfg.are_points_on_device = true; // assuming copy to device memory +auto rv = msm::MSM(coeffs_device, points, msm_size, cfg, results); +``` + +#### Views + +The Polynomial API supports efficient data handling through the use of memory views. These views provide direct access to the polynomial's internal state without the need to copy data. This feature is particularly useful for operations that require direct access to device memory, enhancing both performance and memory efficiency. + +##### What is a Memory View? + +A memory view is essentially a pointer to data stored in device memory. By providing a direct access pathway to the data, it eliminates the need for data duplication, thus conserving both time and system resources. This is especially beneficial in high-performance computing environments where data size and operation speed are critical factors. + +##### Applications of Memory Views + +Memory views are extremely versatile and can be employed in various computational contexts such as: + +- **Commitments**: Views can be used to commit polynomial states in cryptographic schemes, such as Multi-Scalar Multiplications (MSM). +- **External Computations**: They allow external functions or algorithms to utilize the polynomial's data directly, facilitating operations outside the core polynomial API. This is useful for custom operations that are not covered by the API. + +##### Obtaining and Using Views + +To create and use views within the Polynomial API, functions are provided to obtain pointers to both coefficients and evaluation data. Here’s how they are generally structured: + +```cpp +// Obtain a view of the polynomial's coefficients +std::tuple, uint64_t /*size*/, uint64_t /*device_id*/> get_coefficients_view(); +``` + +Example usage: + +```cpp +auto [coeffs_view, size, device_id] = polynomial.get_coefficients_view(); + +// Use coeffs_view in a computational routine that requires direct access to polynomial coefficients +// Example: Passing the view to a GPU-accelerated function +gpu_accelerated_function(coeffs_view.get(),...); +``` + +##### Integrity-Pointer: Managing Memory Views + +Within the Polynomial API, memory views are managed through a specialized tool called the Integrity-Pointer. This pointer type is designed to safeguard operations by monitoring the validity of the memory it points to. It can detect if the memory has been modified or released, thereby preventing unsafe access to stale or non-existent data. +The Integrity-Pointer not only acts as a regular pointer but also provides additional functionality to ensure the integrity of the data it references. Here are its key features: + +```cpp +// Checks whether the pointer is still considered valid +bool isValid() const; + +// Retrieves the raw pointer or nullptr if pointer is invalid +const T* get() const; + +// Dereferences the pointer. Throws exception if the pointer is invalid. +const T& operator*() const; + +//Provides access to the member of the pointed-to object. Throws exception if the pointer is invalid. +const T* operator->() const; +``` + +Consider the Following case: + +```cpp +auto [coeff_view, size, device] = f.get_coefficients_view(); + +// Use the coefficients view to perform external operations +commit_to_polynomial(coeff_view.get(), size); + +// Modification of the original polynomial +f += g; // Any operation that modifies 'f' potentially invalidates 'coeff_view' + +// Check if the view is still valid before using it further +if (coeff_view.isValid()) { + perform_additional_computation(coeff_view.get(), size); +} else { + handle_invalid_data(); +} +``` + + + +## Multi-GPU Support with CUDA Backend + +The Polynomial API includes comprehensive support for multi-GPU environments, a crucial feature for leveraging the full computational power of systems equipped with multiple NVIDIA GPUs. This capability is part of the API's CUDA backend, which is designed to efficiently manage polynomial computations across different GPUs. + +### Setting the CUDA Device + +Like other components of the icicle framework, the Polynomial API allows explicit setting of the current CUDA device: + +```cpp +icicle_set_device(devA); +``` + +This function sets the active CUDA device. All subsequent operations that allocate or deal with polynomial data will be performed on this device. + +### Allocation Consistency + +Polynomials are always allocated on the current CUDA device at the time of their creation. It is crucial to ensure that the device context is correctly set before initiating any operation that involves memory allocation: + +```cpp +// Set the device before creating polynomials +icicle_set_device(devA); +Polynomial p1 = Polynomial::from_coefficients(coeffs, size); + +icicle_set_device(devB); +Polynomial p2 = Polynomial::from_coefficients(coeffs, size); +``` + +### Matching Devices for Operations + +When performing operations that result in the creation of new polynomials (such as addition or multiplication), it is imperative that both operands are on the same CUDA device. If the operands reside on different devices, an exception is thrown: + +```cpp +// Ensure both operands are on the same device +icicle_set_device(devA); +auto p3 = p1 + p2; // Throws an exception if p1 and p2 are not on the same device +``` + +### Device-Agnostic Operations + +Operations that do not involve the creation of new polynomials, such as computing the degree of a polynomial or performing in-place modifications, can be executed regardless of the current device setting: + +```cpp +// 'degree' and in-place operations do not require device matching +int deg = p1.degree(); +p1 += p2; // Valid if p1 and p2 are on the same device, throws otherwise +``` + +### Error Handling + +The API is designed to throw exceptions if operations are attempted across polynomials that are not located on the same GPU. This ensures that all polynomial operations are performed consistently and without data integrity issues due to device mismatches. + +### Best Practices + +To maximize the performance and avoid runtime errors in a multi-GPU setup, always ensure that: + +- The CUDA device is set correctly before polynomial allocation. +- Operations involving new polynomial creation are performed with operands on the same device. + +By adhering to these guidelines, developers can effectively harness the power of multiple GPUs to handle large-scale polynomial computations efficiently. diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/Icicle_Release_README.md b/docs/versioned_docs/version-3.1.0/icicle/primitives/Icicle_Release_README.md new file mode 100644 index 000000000..510a4de7c --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/Icicle_Release_README.md @@ -0,0 +1,89 @@ + +# Icicle Release README + +## Overview + +Icicle is a powerful C++ library designed to provide flexible and efficient computation through its modular backend architecture. This README explains how to build and release Icicle for multiple Linux distributions, including Ubuntu 20.04, Ubuntu 22.04, and CentOS 7. It also describes the content of a release and how to use the generated tar files. + +## Content of a Release + +Each Icicle release includes a tar file containing the build artifacts for a specific distribution. The tar file includes the following structure: + +- **`./icicle/include/`**: This directory contains all the necessary header files for using the Icicle library from C++. + +- **`./icicle/lib/`**: + - **Icicle Libraries**: All the core Icicle libraries are located in this directory. Applications linking to Icicle will use these libraries. + - **Backends**: The `./icicle/lib/backend/` directory houses backend libraries, including the CUDA backend. While the CUDA backend is included, it will only be used on machines with a GPU. On machines without a GPU, the CUDA backend is not utilized. + +### Considerations + +Currently, the CUDA backend is included in every installation tar file, even on machines without a GPU. This ensures consistency across installations but results in additional files being installed that may not be used. + +## Build Docker Image + +To build the Docker images for each distribution and CUDA version, use the following commands: + +```bash +# Ubuntu 22.04, CUDA 12.2 +docker build -t icicle-release-ubuntu22-cuda122 -f Dockerfile.ubuntu22 . + +# Ubuntu 20.04, CUDA 12.2 +docker build -t icicle-release-ubuntu20-cuda122 -f Dockerfile.ubuntu20 . + +# CentOS 7, CUDA 12.2 +docker build -t icicle-release-centos7-cuda122 -f Dockerfile.centos7 . +``` + +### Docker Environment Explanation + +The Docker images you build represent the target environment for the release. Each Docker image is tailored to a specific distribution and CUDA version. You first build the Docker image, which sets up the environment, and then use this Docker image to build the release tar file. This ensures that the build process is consistent and reproducible across different environments. + +## Build Libraries Inside the Docker + +To build the Icicle libraries inside a Docker container and output the tar file to the `release_output` directory: + +```bash +mkdir -p release_output +docker run --rm --gpus all -v ./icicle:/icicle -v ./release_output:/output -v ./scripts:/scripts icicle-release-ubuntu22-cuda122 bash /scripts/release/build_release_and_tar.sh +``` + +This command executes the `build_release_and_tar.sh` script inside the Docker container, which provides the build environment. It maps the source code and output directory to the container, ensuring the generated tar file is available on the host system. + +You can replace `icicle-release-ubuntu22-cuda122` with another Docker image tag to build in the corresponding environment (e.g., Ubuntu 20.04 or CentOS 7). + +## Installing and Using the Release + +1. **Extract the Tar File**: + - Download the appropriate tar file for your distribution (Ubuntu 20.04, Ubuntu 22.04, or CentOS 7). + - Extract it to your desired location: + ```bash + tar -xzvf icicle--cuda122.tar.gz -C /path/to/install/location + ``` + +2. **Linking Your Application**: + - When compiling your C++ application, link against the Icicle libraries found in `./icicle/lib/`: + ```bash + g++ -o myapp myapp.cpp -L/path/to/icicle/lib -licicle_device -licicle_field_or_curve + ``` + - Note: You only need to link to the Icicle device and field or curve libraries. The backend libraries are dynamically loaded at runtime. + +## Backend Loading + +The Icicle library dynamically loads backend libraries at runtime. By default, it searches for backends in the following order: + +1. **Environment Variable**: If the `ICICLE_BACKEND_INSTALL_DIR` environment variable is defined, Icicle will prioritize this location. +2. **Default Directory**: If the environment variable is not set, Icicle will search in the default directory `/opt/icicle/lib/backend`. + +### Custom Backend Loading + +If you need to load a backend from a custom location at any point during runtime, you can call the following function: + +```cpp +extern "C" eIcicleError icicle_load_backend(const char* path, bool is_recursive); +``` + +- **`path`**: The directory where the backend libraries are located. +- **`is_recursive`**: If `true`, the function will search for backend libraries recursively within the specified path. + +--- + diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/hash.md b/docs/versioned_docs/version-3.1.0/icicle/primitives/hash.md new file mode 100644 index 000000000..8651b3376 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/hash.md @@ -0,0 +1,149 @@ +# ICICLE Hashing Logic + +## Overview + +ICICLE’s hashing system is designed to be flexible, efficient, and optimized for both general-purpose and cryptographic operations. Hash functions are essential in operations such as generating commitments, constructing Merkle trees, executing the Sumcheck protocol, and more. + +ICICLE provides an easy-to-use interface for hashing on both CPU and GPU, with transparent backend selection. You can choose between several hash algorithms such as Keccak-256, Keccak-512, SHA3-256, SHA3-512, Blake2s, Poseidon and more, which are optimized for processing both general data and cryptographic field elements or elliptic curve points. + +## Hashing Logic + +Hashing in ICICLE involves creating a hasher instance for the desired algorithm, configuring the hash function if needed, and then processing the data. Data can be provided as strings, arrays, or field elements, and the output is collected in a buffer that automatically adapts to the size of the hashed data. + +## Batch Hashing + +For scenarios where large datasets need to be hashed efficiently, ICICLE supports batch hashing. The batch size is automatically derived from the output size, making it adaptable and optimized for parallel computation on the GPU (when using the CUDA backend). This is useful for Merkle-trees and more. + +## Supported Hash Algorithms + +ICICLE supports the following hash functions: + +1. **Keccak-256** +2. **Keccak-512** +3. **SHA3-256** +4. **SHA3-512** +5. **Blake2s** +6. **Poseidon** + +:::info +Additional hash functions might be added in the future. Stay tuned! +::: + +### Keccak and SHA3 + +[Keccak](https://keccak.team/files/Keccak-implementation-3.2.pdf) is a cryptographic hash function designed by Guido Bertoni, Joan Daemen, Michaël Peeters, and Gilles Van Assche. It was selected as the winner of the NIST hash function competition, becoming the basis for the [SHA-3 standard](https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.202.pdf). + +Keccak can take input messages of any length and produce a fixed-size hash. It uses the sponge construction, which absorbs the input data and squeezes out the final hash value. The permutation function, operating on a state array, applies iterative rounds of operations to derive the hash. + +### Blake2s + +[Blake2s](https://www.rfc-editor.org/rfc/rfc7693.txt) is an optimized cryptographic hash function that provides high performance while ensuring strong security. Blake2s is ideal for hashing small data (such as field elements), especially when speed is crucial. It produces a 256-bit (32-byte) output and is often used in cryptographic protocols. + + +### Poseidon + +[Poseidon](https://eprint.iacr.org/2019/458) is a cryptographic hash function designed specifically for field elements. It is highly optimized for zero-knowledge proofs (ZKPs) and is commonly used in ZK-SNARK systems. Poseidon’s main strength lies in its arithmetization-friendly design, meaning it can be efficiently expressed as arithmetic constraints within a ZK-SNARK circuit. + +Traditional hash functions, such as SHA-2, are difficult to represent within ZK circuits because they involve complex bitwise operations that don’t translate efficiently into arithmetic operations. Poseidon, however, is specifically designed to minimize the number of constraints required in these circuits, making it significantly more efficient for use in ZK-SNARKs and other cryptographic protocols that require hashing over field elements. + +Currently the Poseidon implementation is the Optimized Poseidon (https://hackmd.io/@jake/poseidon-spec#Optimized-Poseidon). Optimized Poseidon significantly decreases the calculation time of the hash. + +The optional `domain_tag` pointer parameter enables domain separation, allowing isolation of hash outputs across different contexts or applications. + +## Using Hash API + +### 1. Creating a Hasher Object + +First, you need to create a hasher object for the specific hash function you want to use: + +```cpp +#include "icicle/hash/keccak.h" +#include "icicle/hash/blake2s.h" +#include "icicle/hash/poseidon.h" + +// Create hasher instances for different algorithms +auto keccak256 = Keccak256::create(); +auto keccak512 = Keccak512::create(); +auto sha3_256 = Sha3_256::create(); +auto sha3_512 = Sha3_512::create(); +auto blake2s = Blake2s::create(); +// Poseidon requires specifying the field-type and t parameter (supported 3,5,9,12) as defined by the Poseidon paper. +auto poseidon = Poseidon::create(t); +// Optionally, Poseidon can accept a domain-tag, which is a field element used to separate applications or contexts. +// The domain tag acts as the first input to the hash function, with the remaining t-1 inputs following it. +scalar_t domain_tag = scalar_t::zero(); // Example using zero; this can be set to any valid field element. +auto poseidon_with_domain_tag = Poseidon::create(t, &domain_tag); +// This version of the hasher with a domain tag expects t-1 additional inputs for hashing. +``` + +### 2. Hashing Data + +Once you have a hasher object, you can hash any input data by passing the input, its size, a configuration, and an output buffer: + +```cpp +/** + * @brief Perform a hash operation. + * + * This function delegates the hash operation to the backend. + * + * @param input Pointer to the input data as bytes. + * @param size The number of bytes to hash. If 0, the default chunk size is used. + * @param config Configuration options for the hash operation. + * @param output Pointer to the output data as bytes. + * @return An error code of type eIcicleError indicating success or failure. + */ +eIcicleError hash(const std::byte* input, uint64_t size, const HashConfig& config, std::byte* output) const; + +/** + * @brief Perform a hash operation using typed data. + * + * Converts input and output types to `std::byte` pointers and delegates the call to the backend. + * + * @tparam PREIMAGE The type of the input data. + * @tparam IMAGE The type of the output data. + * @param input Pointer to the input data. + * @param size The number of elements of type `PREIMAGE` to a single hasher. + * @param config Configuration options for the hash operation. + * @param output Pointer to the output data. + * @return An error code of type eIcicleError indicating success or failure. + */ +template +eIcicleError hash(const PREIMAGE* input, uint64_t size, const HashConfig& config, IMAGE* output) const; +``` + +Example Usage: + +```cpp +// Using the Blake2s hasher +const std::string input = "Hello, I am Blake2s!"; +const uint64_t output_size = 32; // Blake2s outputs 32 bytes +auto output = std::make_unique(output_size); +auto config = default_hash_config(); + +eIcicleErr err = blake2s.hash(input.data(), input.size(), config, output.get()); + +// Alternatively, use another hasher (e.g., Keccak256, SHA3-512) +``` + +### 3. Batch Hashing + +To perform batch hashing, set the `config.batch` field to indicate the number of batches. This allows for multiple inputs to be hashed in parallel: + +```cpp +auto config = default_hash_config(); +config.batch = 2; + +const std::string input = "0123456789abcdef"; // This is a batch of "01234567" and "89abcdef" +auto output = std::make_unique(32 * config.batch); // Allocate output for 2 batches + +eIcicleErr err = keccak256.hash(input.data(), input.size() / config.batch, config, output.get()); +``` + +### 4. Posidon sponge function + +Currently the poseidon sponge function (Sec 2.1 of https://eprint.iacr.org/2019/458.pdf ) isn't implemented. + +### Supported Bindings + +- [Rust](../rust-bindings/hash) +- [Go](../golang-bindings/hash) diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/image-1.png b/docs/versioned_docs/version-3.1.0/icicle/primitives/image-1.png new file mode 100644 index 000000000..cc0ba5916 Binary files /dev/null and b/docs/versioned_docs/version-3.1.0/icicle/primitives/image-1.png differ diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/image-2.png b/docs/versioned_docs/version-3.1.0/icicle/primitives/image-2.png new file mode 100644 index 000000000..e59a16925 Binary files /dev/null and b/docs/versioned_docs/version-3.1.0/icicle/primitives/image-2.png differ diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/image-3.png b/docs/versioned_docs/version-3.1.0/icicle/primitives/image-3.png new file mode 100644 index 000000000..75d3b4329 Binary files /dev/null and b/docs/versioned_docs/version-3.1.0/icicle/primitives/image-3.png differ diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/image.png b/docs/versioned_docs/version-3.1.0/icicle/primitives/image.png new file mode 100644 index 000000000..0fa4826e9 Binary files /dev/null and b/docs/versioned_docs/version-3.1.0/icicle/primitives/image.png differ diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle.md b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle.md new file mode 100644 index 000000000..996a5ef96 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle.md @@ -0,0 +1,273 @@ + +# Merkle Tree API Documentation + +## What is a Merkle Tree? + +A **Merkle tree** is a cryptographic data structure that allows for **efficient verification of data integrity**. It consists of: +- **Leaf nodes**, each containing a piece of data. +- **Internal nodes**, which store the **hashes of their child nodes**, make up the layers leading to the **root node** which is the cryptographic commitment. + + +## Tree Structure and Configuration + +### Structure Definition + +With ICICLE, you have the **flexibility** to build various tree topologies based on your needs. A tree is defined by: + +1. **Hasher per layer** ([Link to Hasher API](./hash.md)) with a **default input size**. +2. **Size of a leaf element** (in bytes): This defines the **granularity** of the data used for opening proofs. + +The **root node** is assumed to be a single node. The **height of the tree** is determined by the **number of layers**. +Each layer's **arity** is calculated as: + +$$ +{arity}_i = \frac{layers[i].inputSize}{layers[i-1].outputSize} +$$ + +For **layer 0**: + +$$ +{arity}_0 = \frac{layers[0].inputSize}{leafSize} +$$ + +:::note +Each layer has a shrinking-factor defined by $\frac{layer.outputSize}{layer.inputSize}$. +This factor is used to compute the input size, assuming a single root node. +::: + +When dealing with very large Merkle trees, storing the entire tree can be memory-intensive. To manage this, ICICLE allows users to store only the upper layers of the tree while omitting the lower layers, which can be recomputed later as needed. This approach conserves memory but requires recomputing the omitted layers when generating Merkle proofs. + + + +### Defining a Merkle Tree + +```cpp +// icicle/merkle/merkle_tree.h +static MerkleTree create( + const std::vector& layer_hashers, + uint64_t leaf_element_size, + uint64_t output_store_min_layer = 0); +``` + +The `output_store_min_layer` parameter defines the lowest layer that will be stored in memory. Layers below this value will not be stored, saving memory at the cost of additional computation when proofs are generated. + + + +### Building the Tree + +The Merkle tree can be constructed from input data of any type, allowing flexibility in its usage. The size of the input must align with the tree structure defined by the hash layers and leaf size. If the input size does not match the expected size, padding may be applied. + +Refer to the [Padding Section](#padding) for more details on how mismatched input sizes are handled. + +```cpp +// icicle/merkle/merkle_tree.h +inline eIcicleError build( + const std::byte* leaves, + uint64_t leaves_size, + const MerkleTreeConfig& config); + +template +inline eIcicleError build( + const T* leaves, + uint64_t nof_leaves, + const MerkleTreeConfig& config); +``` + + + +## Tree Examples + +### Example A: Binary Tree + +A binary tree with **5 layers**, using **Keccak-256**: + +![Merkle Tree Diagram](./merkle_diagrams/diagram1.png) + +```cpp +const uint64_t leaf_size = 1024; +// Allocate a dummy input. It can be any type as long as the total size matches. +const uint32_t max_input_size = leaf_size * 16; +auto input = std::make_unique(max_input_size / sizeof(uint64_t)); + +// Define hashers +auto hash = Keccak256::create(leaf_size); // hash 1KB -> 32B +auto compress = Keccak256::create(2 * hasher.output_size()); // hash every 64B to 32B + +// Construct the tree using the layer hashers and leaf-size +std::vector hashers = {hasher, compress, compress, compress, compress}; +auto merkle_tree = MerkleTree::create(hashers, leaf_size); + +// compute the tree +merkle_tree.build(input.get(), max_input_size / sizeof(uint64_t), default_merkle_tree_config()); +``` + + + +### Example B: Tree with Arity 4 + +This example uses **Blake2s** in the upper layer: + +![Merkle Tree Diagram](./merkle_diagrams/diagram2.png) + +```cpp +#include "icicle/merkle/merkle_tree.h" + +const uint64_t leaf_size = 1024; +const uint32_t max_input_size = leaf_size * 16; +auto input = std::make_unique(max_input_size / sizeof(uint64_t)); + +// note here we use Blake2S for the upper layer +auto hash = Keccak256::create(leaf_size); +auto compress = Blake2s::create(4 * hash.output_size()); + +std::vector hashers = {hash, compress, compress}; +auto merkle_tree = MerkleTree::create(hashers, leaf_size); + +merkle_tree.build(input.get(), max_input_size / sizeof(uint64_t), default_merkle_tree_config()); +``` + +:::note +Any combination of hashers is valid including **Poseidon** that computes on field elements. +::: + + + +## Padding + +:::note +Padding feature is not yet supported in **v3.1** and is planned for **v3.2**. +::: + +When the input for **layer 0** is smaller than expected, ICICLE can apply **padding** to align the data. + +**Padding Schemes:** +1. **Zero padding:** Adds zeroes to the remaining space. +2. **Repeat last leaf:** The final leaf element is repeated to fill the remaining space. + +```cpp +auto config = default_merkle_tree_config(); +config.padding_policy = PaddingPolicy::ZeroPadding; +merkle_tree.build(input.get(), max_input_size / sizeof(uint64_t), config); +``` + + + +## Root as Commitment + +Retrieve the Merkle-root and serialize. + +```cpp +/** + * @brief Returns a pair containing the pointer to the root (ON HOST) data and its size. + * @return A pair of (root data pointer, root size). + */ +inline std::pair get_merkle_root() const; + +auto [commitment, size] = merkle_tree.get_merkle_root(); +serialize_commitment_application_code(...); +``` + +:::warning +The commitment can be serialized to the proof. This is not handled by ICICLE. +::: + + + +## Generating Merkle Proofs + +Merkle proofs are used to **prove the integrity of opened leaves** in a Merkle tree. A proof ensures that a specific leaf belongs to the committed data by enabling the verifier to reconstruct the **root hash (commitment)**. + +A Merkle proof contains: + +- **Leaf**: The data being verified. +- **Index** (leaf_idx): The position of the leaf in the original dataset. +- **Path**: A sequence of sibling hashes (tree nodes) needed to recompute the path from the leaf to the root. + +![Merkle Pruned Phat Diagram](./merkle_diagrams/diagram1_path.png) + + +```cpp +// icicle/merkle/merkle_proof.h +class MerkleProof { + // Represents the Merkle proof with leaf, root, and path data. +}; +``` + +### Example: Generating a Proof + +Generating a proof for leaf idx 3: + +```cpp +MerkleProof proof{}; +auto err = merkle_tree.get_merkle_proof( + input.get(), + max_input_size / sizeof(uint64_t), + 3 /*leaf-idx*/, true, + default_merkle_tree_config(), proof); + +auto [_leaf, _leaf_size, _leaf_idx] = proof.get_leaf(); +auto [_path, _path_size] = proof.get_path(); +``` + +:::warning +The Merkle-path can be serialized to the proof along with the leaf. This is not handled by ICICLE. +::: + + + +## Verifying Merkle Proofs + +```cpp +/** + * @brief Verify an element against the Merkle path using layer hashers. + * @param merkle_proof The MerkleProof object includes the leaf, path, and the root. + * @param valid output valid bit. True if the proof is valid, false otherwise. + */ +eIcicleError verify(const MerkleProof& merkle_proof, bool& valid) const +``` + +### Example: Verifying a Proof + +```cpp +bool valid = false; +auto err = merkle_tree.verify(proof, valid); +``` + + + +## Pruned vs. Full Merkle-paths + +A **Merkle path** is a collection of **sibling hashes** that allows the verifier to **reconstruct the root hash** from a specific leaf. +This enables anyone with the **path and root** to verify that the **leaf** belongs to the committed dataset. +There are two types of paths that can be computed: + +- [**Pruned Path:**](#generating-merkle-proofs) Contains only necessary sibling hashes. +- **Full Path:** Contains all sibling nodes and intermediate hashes. + + +![Merkle Full Path Diagram](./merkle_diagrams/diagram1_path_full.png) + +To compute a full path, specify `pruned=false`: + +```cpp +MerkleProof proof{}; +auto err = merkle_tree.get_merkle_proof( + input.get(), + max_input_size / sizeof(uint64_t), + 3 /*leaf-idx*/, false /*=pruned*/, // --> note the pruned flag here + default_merkle_tree_config(), proof); +``` + + + +## Handling Partial Tree Storage + +In cases where the **Merkle tree is large**, only the **top layers** may be stored to conserve memory. +When opening leaves, the **first layers** (closest to the leaves) are **recomputed dynamically**. + +For example to avoid storing first layer we can define a tree as follows: + +```cpp +const int min_layer_to_store = 1; +auto merkle_tree = MerkleTree::create(hashers, leaf_size, min_layer_to_store); +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1.gv b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1.gv new file mode 100644 index 000000000..c8ebf342f --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1.gv @@ -0,0 +1,115 @@ +digraph MerkleTree { + rankdir = BT; + // Change to bottom-to-top for reversed flow + node [shape = circle; style = filled; color = lightblue; fontname = "Helvetica"; fontsize = 10;]; + + // Root node + Root [label = "Root\n (Commitment) | Keccak256";]; + + // Internal nodes + L1_0 [label = "Keccak256";]; + L1_1 [label = "";]; + + L2_0 [label = "Keccak256";]; + L2_1 [label = "";]; + L2_2 [label = "";]; + L2_3 [label = "";]; + + L3_0 [label = "Keccak256";]; + L3_1 [label = "";]; + L3_2 [label = "";]; + L3_3 [label = "";]; + L3_4 [label = "";]; + L3_5 [label = "";]; + L3_6 [label = "";]; + L3_7 [label = "";]; + + L4_0 [label = "Keccak256";]; + L4_1 [label = "";]; + L4_2 [label = "";]; + L4_3 [label = "";]; + L4_4 [label = "";]; + L4_5 [label = "";]; + L4_6 [label = "";]; + L4_7 [label = "";]; + L4_8 [label = "";]; + L4_9 [label = "";]; + L4_10 [label = "";]; + L4_11 [label = "";]; + L4_12 [label = "";]; + L4_13 [label = "";]; + L4_14 [label = "";]; + L4_15 [label = "";]; + + node [style = filled; fillcolor = lightgreen; shape = rect;]; + // Leaf nodes + Leaf_0 [label = "Leaf-0";]; + Leaf_1 [label = "Leaf-1";]; + Leaf_2 [label = "Leaf-2";]; + Leaf_3 [label = "Leaf-3";]; + Leaf_4 [label = "Leaf-4";]; + Leaf_5 [label = "Leaf-5";]; + Leaf_6 [label = "Leaf-6";]; + Leaf_7 [label = "Leaf-7";]; + Leaf_8 [label = "Leaf-8";]; + Leaf_9 [label = "Leaf-9";]; + Leaf_10 [label = "Leaf-10";]; + Leaf_11 [label = "Leaf-11";]; + Leaf_12 [label = "Leaf-12";]; + Leaf_13 [label = "Leaf-13";]; + Leaf_14 [label = "Leaf-14";]; + Leaf_15 [label = "Leaf-15";]; + + // Connections: Reverse direction from leaves to root + L4_0 -> L3_0; + L4_1 -> L3_0; + L4_2 -> L3_1; + L4_3 -> L3_1; + L4_4 -> L3_2; + L4_5 -> L3_2; + L4_6 -> L3_3; + L4_7 -> L3_3; + L4_8 -> L3_4; + L4_9 -> L3_4; + L4_10 -> L3_5; + L4_11 -> L3_5; + L4_12 -> L3_6; + L4_13 -> L3_6; + L4_14 -> L3_7; + L4_15 -> L3_7; + + L3_0 -> L2_0; + L3_1 -> L2_0; + L3_2 -> L2_1; + L3_3 -> L2_1; + L3_4 -> L2_2; + L3_5 -> L2_2; + L3_6 -> L2_3; + L3_7 -> L2_3; + + L2_0 -> L1_0; + L2_1 -> L1_0; + L2_2 -> L1_1; + L2_3 -> L1_1; + + L1_0 -> Root; + L1_1 -> Root; + + // Leaves connected to layer 4 + Leaf_0 -> L4_0; + Leaf_1 -> L4_1; + Leaf_2 -> L4_2; + Leaf_3 -> L4_3; + Leaf_4 -> L4_4; + Leaf_5 -> L4_5; + Leaf_6 -> L4_6; + Leaf_7 -> L4_7; + Leaf_8 -> L4_8; + Leaf_9 -> L4_9; + Leaf_10 -> L4_10; + Leaf_11 -> L4_11; + Leaf_12 -> L4_12; + Leaf_13 -> L4_13; + Leaf_14 -> L4_14; + Leaf_15 -> L4_15; +} \ No newline at end of file diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1.png b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1.png new file mode 100644 index 000000000..c2260c5af Binary files /dev/null and b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1.png differ diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path.gv b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path.gv new file mode 100644 index 000000000..cc6b941fb --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path.gv @@ -0,0 +1,117 @@ +digraph MerkleTree { + rankdir = BT; + // Bottom-to-top direction for root construction + node [shape = circle; style = filled; color = lightblue; fontname = "Helvetica"; fontsize = 10;]; + + // Root node + Root [label = "Root\n (Commitment)";]; + + // Internal nodes + L1_0 [label = "";]; + L1_1 [label = "path.3";fillcolor = yellow;]; + + L2_0 [label = "";]; + L2_1 [label = "path.2";fillcolor = yellow;]; + L2_2 [label = "";]; + L2_3 [label = "";]; + + L3_0 [label = "path.1";fillcolor = yellow;]; + L3_1 [label = "";]; + L3_2 [label = "";]; + L3_3 [label = "";]; + L3_4 [label = "";]; + L3_5 [label = "";]; + L3_6 [label = "";]; + L3_7 [label = "";]; + + L4_0 [label = "";]; + L4_1 [label = "";]; + L4_2 [label = "path.0";fillcolor = yellow;]; + L4_3 [label = "";]; + L4_4 [label = "";]; + L4_5 [label = "";]; + L4_6 [label = "";]; + L4_7 [label = "";]; + L4_8 [label = "";]; + L4_9 [label = "";]; + L4_10 [label = "";]; + L4_11 [label = "";]; + L4_12 [label = "";]; + L4_13 [label = "";]; + L4_14 [label = "";]; + L4_15 [label = "";]; + + // Leaf nodes (rectangular, green) + node [style = filled; fillcolor = lightgreen; shape = rect;]; + Leaf_0 [label = "Leaf-0";]; + Leaf_1 [label = "Leaf-1";]; + Leaf_2 [label = "Leaf-2";]; + Leaf_3 [label = "Leaf-3";fillcolor = yellow;]; + // Highlighted leaf in path + Leaf_4 [label = "Leaf-4";]; + Leaf_5 [label = "Leaf-5";]; + Leaf_6 [label = "Leaf-6";]; + Leaf_7 [label = "Leaf-7";]; + Leaf_8 [label = "Leaf-8";]; + Leaf_9 [label = "Leaf-9";]; + Leaf_10 [label = "Leaf-10";]; + Leaf_11 [label = "Leaf-11";]; + Leaf_12 [label = "Leaf-12";]; + Leaf_13 [label = "Leaf-13";]; + Leaf_14 [label = "Leaf-14";]; + Leaf_15 [label = "Leaf-15";]; + + // Reverse connections from leaves to root + // Connections: Reverse direction from leaves to root + L4_0 -> L3_0; + L4_1 -> L3_0; + L4_2 -> L3_1; + L4_3 -> L3_1; + L4_4 -> L3_2; + L4_5 -> L3_2; + L4_6 -> L3_3; + L4_7 -> L3_3; + L4_8 -> L3_4; + L4_9 -> L3_4; + L4_10 -> L3_5; + L4_11 -> L3_5; + L4_12 -> L3_6; + L4_13 -> L3_6; + L4_14 -> L3_7; + L4_15 -> L3_7; + + L3_0 -> L2_0; + L3_1 -> L2_0; + L3_2 -> L2_1; + L3_3 -> L2_1; + L3_4 -> L2_2; + L3_5 -> L2_2; + L3_6 -> L2_3; + L3_7 -> L2_3; + + L2_0 -> L1_0; + L2_1 -> L1_0; + L2_2 -> L1_1; + L2_3 -> L1_1; + + L1_0 -> Root; + L1_1 -> Root; + + // Leaves connected to layer 4 + Leaf_0 -> L4_0; + Leaf_1 -> L4_1; + Leaf_2 -> L4_2; + Leaf_3 -> L4_3; + Leaf_4 -> L4_4; + Leaf_5 -> L4_5; + Leaf_6 -> L4_6; + Leaf_7 -> L4_7; + Leaf_8 -> L4_8; + Leaf_9 -> L4_9; + Leaf_10 -> L4_10; + Leaf_11 -> L4_11; + Leaf_12 -> L4_12; + Leaf_13 -> L4_13; + Leaf_14 -> L4_14; + Leaf_15 -> L4_15; +} \ No newline at end of file diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path.png b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path.png new file mode 100644 index 000000000..178d5366e Binary files /dev/null and b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path.png differ diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path_full.gv b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path_full.gv new file mode 100644 index 000000000..e7b60c3d8 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path_full.gv @@ -0,0 +1,113 @@ +digraph MerkleTree { + rankdir = BT; + node [shape = circle; style = filled; color = lightblue; fontname = "Helvetica"; fontsize = 10;]; + + // Root node + Root [label = "Root\n (Commitment)";]; + + L1_0 [label = "path.6";fillcolor = yellow;]; + L1_1 [label = "path.7";fillcolor = yellow;]; + + L2_0 [label = "path.4";fillcolor = yellow;]; + L2_1 [label = "path.5";fillcolor = yellow;]; + L2_2 [label = "";]; + L2_3 [label = "";]; + + L3_0 [label = "path.2";fillcolor = yellow;]; + L3_1 [label = "path.3";fillcolor = yellow;]; + L3_2 [label = "";]; + L3_3 [label = "";]; + L3_4 [label = "";]; + L3_5 [label = "";]; + L3_6 [label = "";]; + L3_7 [label = "";]; + + L4_0 [label = "";]; + L4_1 [label = "";]; + L4_2 [label = "path.0";fillcolor = yellow;]; + L4_3 [label = "path.1";fillcolor = yellow;]; + L4_4 [label = "";]; + L4_5 [label = "";]; + L4_6 [label = "";]; + L4_7 [label = "";]; + L4_8 [label = "";]; + L4_9 [label = "";]; + L4_10 [label = "";]; + L4_11 [label = "";]; + L4_12 [label = "";]; + L4_13 [label = "";]; + L4_14 [label = "";]; + L4_15 [label = "";]; + + node [style = filled; fillcolor = lightgreen; shape = rect;]; + Leaf_0 [label = "Leaf-0";]; + Leaf_1 [label = "Leaf-1";]; + Leaf_2 [label = "Leaf-2";]; + Leaf_3 [label = "Leaf-3";fillcolor = yellow;]; + Leaf_4 [label = "Leaf-4";]; + Leaf_5 [label = "Leaf-5";]; + Leaf_6 [label = "Leaf-6";]; + Leaf_7 [label = "Leaf-7";]; + Leaf_8 [label = "Leaf-8";]; + Leaf_9 [label = "Leaf-9";]; + Leaf_10 [label = "Leaf-10";]; + Leaf_11 [label = "Leaf-11";]; + Leaf_12 [label = "Leaf-12";]; + Leaf_13 [label = "Leaf-13";]; + Leaf_14 [label = "Leaf-14";]; + Leaf_15 [label = "Leaf-15";]; + + + // Connections: Reverse direction from leaves to root + L4_0 -> L3_0; + L4_1 -> L3_0; + L4_2 -> L3_1; + L4_3 -> L3_1; + L4_4 -> L3_2; + L4_5 -> L3_2; + L4_6 -> L3_3; + L4_7 -> L3_3; + L4_8 -> L3_4; + L4_9 -> L3_4; + L4_10 -> L3_5; + L4_11 -> L3_5; + L4_12 -> L3_6; + L4_13 -> L3_6; + L4_14 -> L3_7; + L4_15 -> L3_7; + + L3_0 -> L2_0; + L3_1 -> L2_0; + L3_2 -> L2_1; + L3_3 -> L2_1; + L3_4 -> L2_2; + L3_5 -> L2_2; + L3_6 -> L2_3; + L3_7 -> L2_3; + + L2_0 -> L1_0; + L2_1 -> L1_0; + L2_2 -> L1_1; + L2_3 -> L1_1; + + L1_0 -> Root; + L1_1 -> Root; + + // Leaves connected to layer 4 + Leaf_0 -> L4_0; + Leaf_1 -> L4_1; + Leaf_2 -> L4_2; + Leaf_3 -> L4_3; + Leaf_4 -> L4_4; + Leaf_5 -> L4_5; + Leaf_6 -> L4_6; + Leaf_7 -> L4_7; + Leaf_8 -> L4_8; + Leaf_9 -> L4_9; + Leaf_10 -> L4_10; + Leaf_11 -> L4_11; + Leaf_12 -> L4_12; + Leaf_13 -> L4_13; + Leaf_14 -> L4_14; + Leaf_15 -> L4_15; +} \ No newline at end of file diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path_full.png b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path_full.png new file mode 100644 index 000000000..ec963f5e5 Binary files /dev/null and b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram1_path_full.png differ diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram2.gv b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram2.gv new file mode 100644 index 000000000..fd0800015 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram2.gv @@ -0,0 +1,89 @@ +digraph MerkleTree { + rankdir = BT; + // Reverse the rank direction to bottom-to-top + node [shape = circle; style = filled; color = lightblue; fontname = "Helvetica"; fontsize = 10;]; + + // Root node + Root [label = "Root\n (Commitment) | Blake2s";]; + + L1_0 [label = "Blake2s";]; + L1_1 [label = "";]; + L1_2 [label = "";]; + L1_3 [label = "";]; + + L2_0 [label = "Keccak256";]; + L2_1 [label = "";]; + L2_2 [label = "";]; + L2_3 [label = "";]; + L2_4 [label = "";]; + L2_5 [label = "";]; + L2_6 [label = "";]; + L2_7 [label = "";]; + L2_8 [label = "";]; + L2_9 [label = "";]; + L2_10 [label = "";]; + L2_11 [label = "";]; + L2_12 [label = "";]; + L2_13 [label = "";]; + L2_14 [label = "";]; + L2_15 [label = "";]; + + node [style = filled; fillcolor = lightgreen; shape = rect;]; + Leaf_0 [label = "Leaf-0";]; + Leaf_1 [label = "Leaf-1";]; + Leaf_2 [label = "Leaf-2";]; + Leaf_3 [label = "Leaf-3";]; + Leaf_4 [label = "Leaf-4";]; + Leaf_5 [label = "Leaf-5";]; + Leaf_6 [label = "Leaf-6";]; + Leaf_7 [label = "Leaf-7";]; + Leaf_8 [label = "Leaf-8";]; + Leaf_9 [label = "Leaf-9";]; + Leaf_10 [label = "Leaf-10";]; + Leaf_11 [label = "Leaf-11";]; + Leaf_12 [label = "Leaf-12";]; + Leaf_13 [label = "Leaf-13";]; + Leaf_14 [label = "Leaf-14";]; + Leaf_15 [label = "Leaf-15";]; + + // Connections: Internal nodes to root + L1_0 -> Root; + L1_1 -> Root; + L1_2 -> Root; + L1_3 -> Root; + + L2_0 -> L1_0; + L2_1 -> L1_0; + L2_2 -> L1_0; + L2_3 -> L1_0; + L2_4 -> L1_1; + L2_5 -> L1_1; + L2_6 -> L1_1; + L2_7 -> L1_1; + L2_8 -> L1_2; + L2_9 -> L1_2; + L2_10 -> L1_2; + L2_11 -> L1_2; + L2_12 -> L1_3; + L2_13 -> L1_3; + L2_14 -> L1_3; + L2_15 -> L1_3; + + // Connections: Leaves to internal nodes + Leaf_0 -> L2_0; + Leaf_1 -> L2_1; + Leaf_2 -> L2_2; + Leaf_3 -> L2_3; + Leaf_4 -> L2_4; + Leaf_5 -> L2_5; + Leaf_6 -> L2_6; + Leaf_7 -> L2_7; + Leaf_8 -> L2_8; + Leaf_9 -> L2_9; + Leaf_10 -> L2_10; + Leaf_11 -> L2_11; + Leaf_12 -> L2_12; + Leaf_13 -> L2_13; + Leaf_14 -> L2_14; + Leaf_15 -> L2_15; +} \ No newline at end of file diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram2.png b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram2.png new file mode 100644 index 000000000..2fd2d95d6 Binary files /dev/null and b/docs/versioned_docs/version-3.1.0/icicle/primitives/merkle_diagrams/diagram2.png differ diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/msm.md b/docs/versioned_docs/version-3.1.0/icicle/primitives/msm.md new file mode 100644 index 000000000..4328d0fed --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/msm.md @@ -0,0 +1,203 @@ + +# MSM - Multi Scalar Multiplication + +## Overview + +Multi-Scalar Multiplication (MSM) is a fundamental operation in elliptic curve cryptography and zero-knowledge proofs. It is defined as: + +$$ +MSM(a, G) = \sum_{j=0}^{n-1} a_j \cdot G_j +$$ + +Where: +- $G_j \in G$ are points from an elliptic curve group. +- $a_0, \ldots, a_n$ are scalars. +- $MSM(a, G) \in G$ is the result, a single elliptic curve point. + +MSM is inherently parallelizable, making it a critical operation for optimizing performance in cryptographic protocols like zk-SNARKs. Accelerating MSM can significantly reduce the time required for proof generation. + + +Accelerating MSM is crucial to a ZK protocol's performance due to the [large percent of run time](https://hackmd.io/@0xMonia/SkQ6-oRz3#Hardware-acceleration-in-action) they take when generating proofs. +You can learn more about how MSMs work from this [video](https://www.youtube.com/watch?v=Bl5mQA7UL2I) and from our resource list on [Ingopedia](https://www.ingonyama.com/ingopedia/msm). + +## C++ API + +### `MSMConfig` Struct + +The `MSMConfig` struct configures the MSM operation. It allows customization of parameters like the number of precomputed points, the window bitsize (`c`), and memory management. Here's the configuration structure: + +```cpp +struct MSMConfig { + icicleStreamHandle stream; + int precompute_factor; + int c; + int bitsize; + int batch_size; + bool are_points_shared_in_batch; + bool are_scalars_on_device; + bool are_scalars_montgomery_form; + bool are_points_on_device; + bool are_points_montgomery_form; + bool are_results_on_device; + bool is_async; + ConfigExtension* ext; +}; +``` + +#### Default Configuration + +You can obtain a default `MSMConfig` using: + +```cpp + static MSMConfig default_msm_config() + { + MSMConfig config = { + nullptr, // stream + 1, // precompute_factor + 0, // c + 0, // bitsize + 1, // batch_size + true, // are_points_shared_in_batch + false, // are_scalars_on_device + false, // are_scalars_montgomery_form + false, // are_points_on_device + false, // are_points_montgomery_form + false, // are_results_on_device + false, // is_async + nullptr, // ext + }; + return config; + } +``` + +### `msm` Function + +The `msm` function computes the MSM operation: + +```cpp +template +eIcicleError msm(const S* scalars, const A* bases, int msm_size, const MSMConfig& config, P* results); +``` + +:::note +The API is template and can work with all ICICLE curves (if corresponding lib is linked), including G2 groups. +::: + +### Batched MSM + +The MSM supports batch mode - running multiple MSMs in parallel. It's always better to use the batch mode instead of running single msms in serial as long as there is enough memory available. We support running a batch of MSMs that share the same points as well as a batch of MSMs that use different points. + +Config fields `are_points_shared_in_batch` and `batch_size` are used to configure msm for batch mode. + +### G2 MSM + +for G2 MSM, use the [same msm api](#msm-function) with the G2 types. + +:::note +Supported curves have types for both G1 and G2. +::: + +### Precompution + +#### What It Does: + +- The function computes a set of additional points derived from the original base points. These precomputed points are stored and later reused during the MSM computation. +- Purpose: By precomputing and storing these points, the MSM operation can reduce the number of operations needed at runtime, which can significantly speed up the calculation. + +#### When to Use: + +- Memory vs. Speed Trade-off: Precomputation increases the memory footprint because additional points are stored, but it reduces the computational effort during MSM, making the process faster. +- Best for Repeated Use: It’s especially beneficial when the same set of base points is used multiple times in different MSM operations. + +```cpp +template +eIcicleError msm_precompute_bases(const A* input_bases, int bases_size, const MSMConfig& config, A* output_bases); +``` + +:::note +User is allocating the `output_bases` (on host or device memory) and later use it as bases when calling msm. +::: + +## Rust and Go bindings + +The Rust and Go bindings provide equivalent functionality for their respective environments. Refer to their documentation for details on usage. + +- [Golang](../golang-bindings/msm.md) +- [Rust](../rust-bindings/msm.md) + +## CUDA backend MSM +This section describes the CUDA msm implementation and how to customize it (optional). + +### Algorithm description + +We follow the bucket method algorithm. The GPU implementation consists of four phases: + +1. Preparation phase - The scalars are split into smaller scalars of `c` bits each. These are the bucket indices. The points are grouped according to their corresponding bucket index and the buckets are sorted by size. +2. Accumulation phase - Each bucket accumulates all of its points using a single thread. More than one thread is assigned to large buckets, in proportion to their size. A bucket is considered large if its size is above the large bucket threshold that is determined by the `large_bucket_factor` parameter. The large bucket threshold is the expected average bucket size times the `large_bucket_factor` parameter. +3. Buckets Reduction phase - bucket results are multiplied by their corresponding bucket number and each bucket module is reduced to a small number of final results. By default, this is done by an iterative algorithm which is highly parallel. Setting `is_big_triangle` to `true` will switch this phase to the running sum algorithm described in the above YouTube talk which is much less parallel. +4. Final accumulation phase - The final results from the last phase are accumulated using the double-and-add algorithm. + +## Configuring CUDA msm +Use `ConfigExtension` object to pass backend specific configuration. +CUDA specific msm configuration: + +```cpp +ConfigExtension ext; +ext.set("large_bucket_factor", 15); +// use the config-extension in the msm config for the backend to see. +msm_config.ext = &ext; +// call msm +msm(..., config,...); // msm backend is reading the config-extension +``` + +### Choosing optimal parameters + +`is_big_triangle` should be `false` in almost all cases. It might provide better results only for very small MSMs (smaller than 2^8) with a large batch (larger than 100) but this should be tested per scenario. +Large buckets exist in two cases: +1. When the scalar distribution isn't uniform. +2. When `c` does not divide the scalar bit-size. + +`large_bucket_factor` that is equal to 10 yields good results for most cases, but it's best to fine tune this parameter per `c` and per scalar distribution. +The two most important parameters for performance are `c` and the `precompute_factor`. They affect the number of EC additions as well as the memory size. When the points are not known in advance we cannot use precomputation. In this case the best `c` value is usually around $log_2(msmSize) - 4$. However, in most protocols the points are known in advanced and precomputation can be used unless limited by memory. Usually it's best to use maximum precomputation (such that we end up with only a single bucket module) combined we a `c` value around $log_2(msmSize) - 1$. + +## Memory usage estimation + +The main memory requirements of the MSM are the following: + +- Scalars - `sizeof(scalar_t) * msm_size * batch_size` +- Scalar indices - `~6 * sizeof(unsigned) * nof_bucket_modules * msm_size * batch_size` +- Points - `sizeof(affine_t) * msm_size * precomp_factor * batch_size` +- Buckets - `sizeof(projective_t) * nof_bucket_modules * 2^c * batch_size` + +where `nof_bucket_modules = ceil(ceil(bitsize / c) / precompute_factor)` + +During the MSM computation first the memory for scalars and scalar indices is allocated, then the indices are freed and points and buckets are allocated. This is why a good estimation for the required memory is the following formula: + +$max(scalars + scalarIndices, scalars + points + buckets)$ + +This gives a good approximation within 10% of the actual required memory for most cases. + +## Example parameters + +Here is a useful table showing optimal parameters for different MSMs. They are optimal for BLS12-377 curve when running on NVIDIA GeForce RTX 3090 Ti. This is the configuration used: + +Here are the parameters and the results for the different cases: + +| MSM size | Batch size | Precompute factor | c | Memory estimation (GB) | Actual memory (GB) | Single MSM time (ms) | +| -------- | ---------- | ----------------- | --- | ---------------------- | ------------------ | -------------------- | +| 10 | 1 | 1 | 9 | 0.00227 | 0.00277 | 9.2 | +| 10 | 1 | 23 | 11 | 0.00259 | 0.00272 | 1.76 | +| 10 | 1000 | 1 | 7 | 0.94 | 1.09 | 0.051 | +| 10 | 1000 | 23 | 11 | 2.59 | 2.74 | 0.025 | +| 15 | 1 | 1 | 11 | 0.011 | 0.019 | 9.9 | +| 15 | 1 | 16 | 16 | 0.061 | 0.065 | 2.4 | +| 15 | 100 | 1 | 11 | 1.91 | 1.92 | 0.84 | +| 15 | 100 | 19 | 14 | 6.32 | 6.61 | 0.56 | +| 18 | 1 | 1 | 14 | 0.128 | 0.128 | 14.4 | +| 18 | 1 | 15 | 17 | 0.40 | 0.42 | 5.9 | +| 22 | 1 | 1 | 17 | 1.64 | 1.65 | 68 | +| 22 | 1 | 13 | 21 | 5.67 | 5.94 | 54 | +| 24 | 1 | 1 | 18 | 6.58 | 6.61 | 232 | +| 24 | 1 | 7 | 21 | 12.4 | 13.4 | 199 | + +The optimal values can vary per GPU and per curve. It is best to try a few combinations until you get the best results for your specific case. diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/ntt.md b/docs/versioned_docs/version-3.1.0/icicle/primitives/ntt.md new file mode 100644 index 000000000..967cc0e6a --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/ntt.md @@ -0,0 +1,322 @@ +# NTT - Number Theoretic Transform + +## Overview + +The Number Theoretic Transform (NTT) is a variant of the Fourier Transform used over finite fields, particularly those of integers modulo a prime number. NTT operates in a discrete domain and is used primarily in applications requiring modular arithmetic, such as cryptography and polynomial multiplication. + +NTT is defined similarly to the Discrete Fourier Transform (DFT), but instead of using complex roots of unity, it uses roots of unity within a finite field. The definition hinges on the properties of the finite field, specifically the existence of a primitive root of unity of order $N$ (where $N$ is typically a power of 2), and the modulo operation is performed with respect to a specific prime number that supports these roots. + +Formally, given a sequence of integers $a_0, a_1, ..., a_{N-1}$, the NTT of this sequence is another sequence of integers $A_0, A_1, ..., A_{N-1}$, computed as follows: + +$$ +A_k = \sum_{n=0}^{N-1} a_n \cdot \omega^{nk} \mod p +$$ + +where: + +- $N$ is the size of the input sequence and is a power of 2, +- $p$ is a prime number such that $p = kN + 1$ for some integer $k$, ensuring that $p$ supports the existence of $N$th roots of unity, +- $\omega$ is a primitive $N$th root of unity modulo $p$, meaning $\omega^N \equiv 1 \mod p$ and no smaller positive power of $\omega$ is congruent to 1 modulo $p$, +- $k$ ranges from 0 to $N-1$, and it indexes the output sequence. + +NTT is particularly useful because it enables efficient polynomial multiplication under modulo arithmetic, crucial for algorithms in cryptographic protocols and other areas requiring fast modular arithmetic operations. + +There exists also INTT which is the inverse operation of NTT. INTT can take as input an output sequence of integers from an NTT and reconstruct the original sequence. + +## C++ API + +### Ordering + +The `Ordering` enum defines how inputs and outputs are arranged for the NTT operation, offering flexibility in handling data according to different algorithmic needs or compatibility requirements. It primarily affects the sequencing of data points for the transform, which can influence both performance and the compatibility with certain algorithmic approaches. The available ordering options are: + +- **`kNN` (Natural-Natural):** Both inputs and outputs are in their natural order. This is the simplest form of ordering, where data is processed in the sequence it is given, without any rearrangement. + +- **`kNR` (Natural-Reversed):** Inputs are in natural order, while outputs are in bit-reversed order. This ordering is typically used in algorithms that benefit from having the output in a bit-reversed pattern. + +- **`kRN` (Reversed-Natural):** Inputs are in bit-reversed order, and outputs are in natural order. This is often used with the Cooley-Tukey FFT algorithm. + +- **`kRR` (Reversed-Reversed):** Both inputs and outputs are in bit-reversed order. + +- **`kNM` (Natural-Mixed):** Inputs are provided in their natural order, while outputs are arranged in a digit-reversed (mixed) order. This ordering is good for mixed radix NTT operations, where the mixed or digit-reversed ordering of outputs is a generalization of the bit-reversal pattern seen in simpler, radix-2 cases. + +- **`kMN` (Mixed-Natural):** Inputs are in a digit-reversed (mixed) order, while outputs are restored to their natural order. This ordering would primarily be used for mixed radix NTT + +Choosing an algorithm is heavily dependent on your use case. For example Cooley-Tukey will often use `kRN` and Gentleman-Sande often uses `kNR`. + +```cpp +enum class Ordering { + kNN, /**< Inputs and outputs are in natural-order. */ + kNR, /**< Inputs are in natural-order and outputs are in bit-reversed-order. */ + kRN, /**< Inputs are in bit-reversed-order and outputs are in natural-order. */ + kRR, /**< Inputs and outputs are in bit-reversed-order. */ + kNM, /**< Inputs are in natural-order and outputs are in digit-reversed-order. */ + kMN /**< Inputs are in digit-reversed-order and outputs are in natural-order. */ +}; +``` + +### `NTTConfig` Struct + +The `NTTConfig` struct configures the NTT operation. It allows customization of parameters like the batch size, column batch computation, order of inputs and outputs etc. + +```cpp + template + struct NTTConfig { + icicleStreamHandle stream; + S coset_gen; + int batch_size; + bool columns_batch; + Ordering ordering; + bool are_inputs_on_device; + bool are_outputs_on_device; + bool is_async; + ConfigExtension* ext = nullptr; + }; +``` + +#### Default configuration + +You can obtain a default `NTTConfig` using: +```cpp +template +static NTTConfig default_ntt_config() +{ + NTTConfig config = { + nullptr, // stream + S::one(), // coset_gen + 1, // batch_size + false, // columns_batch + Ordering::kNN, // ordering + false, // are_inputs_on_device + false, // are_outputs_on_device + false, // is_async + }; + return config; +} +``` + +### NTT domain +Before computing an NTT, it is mandatory to initialize the roots of unity domain for computing the NTT. + +:::note +NTT domain is constructed for a given size $2^N$ and can be used for any NTT of size smaller or equal to $2^N$. For example a domain of size 32 can be used to compute NTTs of size 2,4,8,16,32. +::: + +```cpp +template +eIcicleError ntt_init_domain(const S& primitive_root, const NTTInitDomainConfig& config); +``` + +:::note +Domain is constructed per device. When using multiple devices (e.g. GPUs), need to call it per device prior to calling ntt. +::: + +To retrieve a root of unity from the domain: +```cpp +template S get_root_of_unity(uint64_t max_size); +``` + +Finally, release the domain to free up device memory when not required: +```cpp +template eIcicleError ntt_release_domain(); +``` + +where + +```cpp +struct NTTInitDomainConfig { + icicleStreamHandle stream; /**< Stream for asynchronous execution. */ + bool is_async; /**< True if operation is asynchronous. Default value is false. */ + ConfigExtension* ext = nullptr; /**< Backend-specific extensions. */ +}; + +static NTTInitDomainConfig default_ntt_init_domain_config() +{ + NTTInitDomainConfig config = { + nullptr, // stream + false // is_async + }; + return config; +} +``` + +### `ntt` Function + +the `ntt` function computes the NTT operation: + +```cpp +template +eIcicleError ntt(const E* input, int size, NTTDir dir, const NTTConfig& config, E* output); + +// Where NTTDir specific whether it is a forward or inverse transform +enum class NTTDir { + kForward, /**< Perform forward NTT. */ + kInverse /**< Perform inverse NTT (iNTT). */ +}; +``` +### EC-NTT +[The ntt api](#ntt-function) works for ECNTT too, given correct types, for supported curves. + +### Batch NTT + +Batch NTT allows you to compute many NTTs with a single API call. Batch NTT can significantly reduce read/write times as well as computation overhead by executing multiple NTT operations in parallel. Batch mode may also offer better utilization of computational resources (memory and compute). + +To compute a batch, set the `batch_size` and `columns_batch` fields of the config struct. + +### Rust and Go bindings + +- [Golang](../golang-bindings/ntt.md) +- [Rust](../rust-bindings/ntt.md) + +### Example + +The following example demonstartes how to use ntt and how pass custom configurations to the CUDA backend. Details are discussed below. + +```cpp +#include "icicle/backend/ntt_config.h" + +// allocate and init input/output +int batch_size = /*...*/; +int log_ntt_size = /*...*/; +int ntt_size = 1 << log_ntt_size; +auto input = std::make_unique(batch_size * ntt_size); +auto output = std::make_unique(batch_size * ntt_size); +initialize_input(ntt_size, batch_size, input.get()); + +// Initialize NTT domain with fast twiddles (CUDA backend) +scalar_t basic_root = scalar_t::omega(log_ntt_size); +auto ntt_init_domain_cfg = default_ntt_init_domain_config(); +ConfigExtension backend_cfg_ext; +backend_cfg_ext.set(CudaBackendConfig::CUDA_NTT_FAST_TWIDDLES_MODE, true); +ntt_init_domain_cfg.ext = &backend_cfg_ext; +ntt_init_domain(basic_root, ntt_init_domain_cfg); + +// ntt configuration +NTTConfig config = default_ntt_config(); +ConfigExtension ntt_cfg_ext; +config.batch_size = batch_size; + +// Compute NTT with explicit selection of Mixed-Radix algorithm. +ntt_cfg_ext.set(CudaBackendConfig::CUDA_NTT_ALGORITHM, CudaBackendConfig::NttAlgorithm::MixedRadix); +config.ext = &ntt_cfg_ext; +ntt(input.get(), ntt_size, NTTDir::kForward, config, output.get()); +``` + +### CUDA backend NTT +This section describes the CUDA ntt implementation and how to use it. + +Our CUDA NTT implementation supports two algorithms `radix-2` and `mixed-radix`. + +### Radix 2 + +At its core, the Radix-2 NTT algorithm divides the problem into smaller sub-problems, leveraging the properties of "divide and conquer" to reduce the overall computational complexity. The algorithm operates on sequences whose lengths are powers of two. + +1. **Input Preparation:** + The input is a sequence of integers $a_0, a_1, \ldots, a_{N-1}, \text{ where } N$ is a power of two. + +2. **Recursive Decomposition:** + The algorithm recursively divides the input sequence into smaller sequences. At each step, it separates the sequence into even-indexed and odd-indexed elements, forming two subsequences that are then processed independently. + +3. **Butterfly Operations:** + The core computational element of the Radix-2 NTT is the "butterfly" operation, which combines pairs of elements from the sequences obtained in the decomposition step. + + Each butterfly operation involves multiplication by a "twiddle factor," which is a root of unity in the finite field, and addition or subtraction of the results, all performed modulo the prime modulus. + + $$ + X_k = (A_k + B_k \cdot W^k) \mod p + $$ + + $X_k$ - The output of the butterfly operation for the $k$-th element + + $A_k$ - an element from the even-indexed subset + + $B_k$ - an element from the odd-indexed subset + + $p$ - prime modulus + + $k$ - The index of the current operation within the butterfly or the transform stage + + The twiddle factors are precomputed to save runtime and improve performance. + +4. **Bit-Reversal Permutation:** + A final step involves rearranging the output sequence into the correct order. Due to the halving process in the decomposition steps, the elements of the transformed sequence are initially in a bit-reversed order. A bit-reversal permutation is applied to obtain the final sequence in natural order. + +### Mixed Radix + +The Mixed Radix NTT algorithm extends the concepts of the Radix-2 algorithm by allowing the decomposition of the input sequence based on various factors of its length. Specifically ICICLEs implementation splits the input into blocks of sizes 16, 32, or 64 compared to radix2 which is always splitting such that we end with NTT of size 2. This approach offers enhanced flexibility and efficiency, especially for input sizes that are composite numbers, by leveraging the "divide and conquer" strategy across multiple radices. + +The NTT blocks in Mixed Radix are implemented more efficiently based on winograd NTT but also optimized memory and register usage is better compared to Radix-2. + +Mixed Radix can reduce the number of stages required to compute for large inputs. + +1. **Input Preparation:** + The input to the Mixed Radix NTT is a sequence of integers $a_0, a_1, \ldots, a_{N-1}$, where $N$ is not strictly required to be a power of two. Instead, $N$ can be any composite number, ideally factorized into primes or powers of primes. + +2. **Factorization and Decomposition:** + Unlike the Radix-2 algorithm, which strictly divides the computational problem into halves, the Mixed Radix NTT algorithm implements a flexible decomposition approach which isn't limited to prime factorization. + + For example, an NTT of size 256 can be decomposed into two stages of $16 \times \text{NTT}_{16}$, leveraging a composite factorization strategy rather than decomposing into eight stages of $\text{NTT}_{2}$. This exemplifies the use of composite factors (in this case, $256 = 16 \times 16$) to apply smaller NTT transforms, optimizing computational efficiency by adapting the decomposition strategy to the specific structure of $N$. + +3. **Butterfly Operations with Multiple Radices:** + The Mixed Radix algorithm utilizes butterfly operations for various radix sizes. Each sub-transform involves specific butterfly operations characterized by multiplication with twiddle factors appropriate for the radix in question. + + The generalized butterfly operation for a radix-$r$ element can be expressed as: + + $$ + X_{k,r} = \sum_{j=0}^{r-1} (A_{j,k} \cdot W^{jk}) \mod p + $$ + + where: + + $X_{k,r}$ - is the output of the $radix-r$ butterfly operation for the $k-th$ set of inputs + + $A_{j,k}$ - represents the $j-th$ input element for the $k-th$ operation + + $W$ - is the twiddle factor + + $p$ - is the prime modulus + +4. **Recombination and Reordering:** + After applying the appropriate butterfly operations across all decomposition levels, the Mixed Radix algorithm recombines the results into a single output sequence. Due to the varied sizes of the sub-transforms, a more complex reordering process may be required compared to Radix-2. This involves digit-reversal permutations to ensure that the final output sequence is correctly ordered. + +### Which algorithm should I choose ? + +Both work only on inputs of power of 2 (e.g., 256, 512, 1024). + +Radix 2 is faster for small NTTs. A small NTT would be around logN = 16 and batch size 1. Radix 2 won't necessarily perform better for smaller `logn` with larger batches. + +Mixed radix on the other hand works better for larger NTTs with larger input sizes. + +Performance really depends on logn size, batch size, ordering, inverse, coset, coeff-field and which GPU you are using. + +For this reason we implemented our [heuristic auto-selection](https://github.com/ingonyama-zk/icicle/blob/main/icicle/src/ntt/ntt.cu#L573) which should choose the most efficient algorithm in most cases. + +We still recommend you benchmark for your specific use case if you think a different configuration would yield better results. + +To Explicitly choose the algorithm: + +```cpp +#include "icicle/backend/ntt_config.h" + +NTTConfig config = default_ntt_config(); +ConfigExtension ntt_cfg_ext; +ntt_cfg_ext.set(CudaBackendConfig::CUDA_NTT_ALGORITHM, CudaBackendConfig::NttAlgorithm::MixedRadix); +config.ext = &ntt_cfg_ext; +ntt(input.get(), ntt_size, NTTDir::kForward, config, output.get()); +``` + + +### Fast twiddles + +When using the Mixed-radix algorithm, it is recommended to initialize the domain in "fast-twiddles" mode. This is essentially allocating the domain using extra memory but enables faster ntt. +To do so simply, pass this flag to the CUDA backend. + +```cpp +#include "icicle/backend/ntt_config.h" + +scalar_t basic_root = scalar_t::omega(log_ntt_size); +auto ntt_init_domain_cfg = default_ntt_init_domain_config(); +ConfigExtension backend_cfg_ext; +backend_cfg_ext.set(CudaBackendConfig::CUDA_NTT_FAST_TWIDDLES_MODE, true); +ntt_init_domain_cfg.ext = &backend_cfg_ext; +ntt_init_domain(basic_root, ntt_init_domain_cfg); +``` \ No newline at end of file diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/overview.md b/docs/versioned_docs/version-3.1.0/icicle/primitives/overview.md new file mode 100644 index 000000000..6c2802861 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/overview.md @@ -0,0 +1,12 @@ +# ICICLE Compute APIs + +This section of the documentation is dedicated to the main APIs provided by ICICLE. We will cover the usage and internal details of our core primitives, such as Multi-Scalar Multiplication (MSM), Number Theoretic Transform (NTT), and various hashing algorithms. Each primitive has its own dedicated page with examples and explanations for C++, Rust, and Go. + +## Supported primitives + +- [MSM](./msm.md) +- [NTT](./ntt.md) +- [Vector Operations](./vec_ops.md) +- [Polynomials](../polynomials/overview.md) +- [Hash](./hash.md) +- [Merkle-tree commitment](./merkle.md) diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/poseidon.md b/docs/versioned_docs/version-3.1.0/icicle/primitives/poseidon.md new file mode 100644 index 000000000..37ea7455a --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/poseidon.md @@ -0,0 +1,218 @@ +# Poseidon + +TODO update for V3 + +[Poseidon](https://eprint.iacr.org/2019/458.pdf) is a popular hash in the ZK ecosystem primarily because its optimized to work over large prime fields, a common setting for ZK proofs, thereby minimizing the number of multiplicative operations required. + +Poseidon has also been specifically designed to be efficient when implemented within ZK circuits, Poseidon uses far less constraints compared to other hash functions like Keccak or SHA-256 in the context of ZK circuits. + +Poseidon has been used in many popular ZK protocols such as Filecoin and [Plonk](https://drive.google.com/file/d/1bZZvKMQHaZGA4L9eZhupQLyGINkkFG_b/view?usp=drive_open). + +Our implementation of Poseidon is implemented in accordance with the optimized [Filecoin version](https://spec.filecoin.io/algorithms/crypto/poseidon/). + +Lets understand how Poseidon works. + +## Initialization + +Poseidon starts with the initialization of its internal state, which is composed of the input elements and some pre-generated constants. An initial round constant is added to each element of the internal state. Adding the round constants ensures the state is properly mixed from the beginning. + +This is done to prevent collisions and to prevent certain cryptographic attacks by ensuring that the internal state is sufficiently mixed and unpredictable. + +![Poseidon initialization of internal state added with pre-generated round constants](https://github.com/ingonyama-zk/icicle/assets/122266060/52257f5d-6097-47c4-8f17-7b6449b9d162) + +## Applying full and partial rounds + +To generate a secure hash output, the algorithm goes through a series of "full rounds" and "partial rounds" as well as transformations between these sets of rounds in the following order: + +```First full rounds -> apply S-box and Round constants -> partial rounds -> Last full rounds -> Apply S-box``` + +### Full rounds + +![Full round iterations consisting of S box operations, adding round constants, and a Full MDS matrix multiplication](https://github.com/ingonyama-zk/icicle/assets/122266060/e4ce0e98-b90b-4261-b83e-3cd8cce069cb) + +**Uniform Application of S-box:** In full rounds, the S-box (a non-linear transformation) is applied uniformly to every element of the hash function's internal state. This ensures a high degree of mixing and diffusion, contributing to the hash function's security. The functions S-box involves raising each element of the state to a certain power denoted by `α` a member of the finite field defined by the prime `p`; `α` can be different depending on the implementation and user configuration. + +**Linear Transformation:** After applying the S-box, a linear transformation is performed on the state. This involves multiplying the state by a MDS (Maximum Distance Separable) Matrix. which further diffuses the transformations applied by the S-box across the entire state. + +**Addition of Round Constants:** Each element of the state is then modified by adding a unique round constant. These constants are different for each round and are precomputed as part of the hash function's initialization. The addition of round constants ensures that even minor changes to the input produce significant differences in the output. + +### Partial Rounds + +![Partial round iterations consisting of selective S box operation, adding a round constant and performing an MDS multiplication with a sparse matrix](https://github.com/ingonyama-zk/icicle/assets/122266060/e8c198b4-7aa4-4b4d-9ec4-604e39e07692) + +**Selective Application of S-Box:** Partial rounds apply the S-box transformation to only one element of the internal state per round, rather than to all elements. This selective application significantly reduces the computational complexity of the hash function without compromising its security. The choice of which element to apply the S-box to can follow a specific pattern or be fixed, depending on the design of the hash function. + +**Linear Transformation and Round Constants:** A linear transformation is performed and round constants are added. The linear transformation in partial rounds can be designed to be less computationally intensive (this is done by using a sparse matrix) than in full rounds, further optimizing the function's efficiency. + +The user of Poseidon can often choose how many partial or full rounds he wishes to apply; more full rounds will increase security but degrade performance. The choice and balance is highly dependent on the use case. + +## Using Poseidon + +ICICLE Poseidon is implemented for GPU and parallelization is performed for each element of the state rather than for each state. +What that means is we calculate multiple hash-sums over multiple pre-images in parallel, rather than going block by block over the input vector. + +So for Poseidon of arity 2 and input of size 1024 * 2, we would expect 1024 elements of output. Which means each block would be of size 2 and that would result in 1024 Poseidon hashes being performed. + +### Supported Bindings + +[`Go`](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/golang/curves/bn254/poseidon/poseidon.go) +[`Rust`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/icicle-core/src/poseidon) + +### Constants + +Poseidon is extremely customizable and using different constants will produce different hashes, security levels and performance results. + +We support pre-calculated and optimized constants for each of the [supported curves](../libraries#supported-curves-and-operations).The constants can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/poseidon/constants) and are labeled clearly per curve `_poseidon.h`. + +If you wish to generate your own constants you can use our python script which can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/poseidon/constants/generate_parameters.py). + +Prerequisites: + +- Install python 3 +- `pip install poseidon-hash` +- `pip install galois==0.3.7` +- `pip install numpy` + +You will then need to modify the following values before running the script. + +```python +# Modify these +arity = 11 # we support arity 2, 4, 8 and 11. +p = 0x73EDA753299D7D483339D80809A1D80553BDA402FFFE5BFEFFFFFFFF00000001 # bls12-381 +# p = 0x12ab655e9a2ca55660b44d1e5c37b00159aa76fed00000010a11800000000001 # bls12-377 +# p = 0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001 # bn254 +# p = 0x1ae3a4617c510eac63b05c06ca1493b1a22d9f300f5138f1ef3622fba094800170b5d44300000008508c00000000001 # bw6-761 +prime_bit_len = 255 +field_bytes = 32 + +... + +# primitive_element = None +primitive_element = 7 # bls12-381 +# primitive_element = 22 # bls12-377 +# primitive_element = 5 # bn254 +# primitive_element = 15 # bw6-761 +``` + +### Rust API + +This is the most basic way to use the Poseidon API. + +```rust +let test_size = 1 << 10; +let arity = 2u32; +let ctx = get_default_device_context(); +let poseidon = Poseidon::load(arity, &ctx).unwrap(); +let config = HashConfig::default(); + +let inputs = vec![F::one(); test_size * arity as usize]; +let outputs = vec![F::zero(); test_size]; +let mut input_slice = HostOrDeviceSlice::on_host(inputs); +let mut output_slice = HostOrDeviceSlice::on_host(outputs); + +poseidon.hash_many::( + &mut input_slice, + &mut output_slice, + test_size as u32, + arity as u32, + 1, // Output length + &config, +) +.unwrap(); +``` + +The `HashConfig` can be modified, by default the inputs and outputs are set to be on `Host` for example. + +```rust +impl<'a> Default for HashConfig<'a> { + fn default() -> Self { + let ctx = get_default_device_context(); + Self { + ctx, + are_inputs_on_device: false, + are_outputs_on_device: false, + is_async: false, + } + } +} +``` + +In the example above `Poseidon::load(arity, &ctx).unwrap();` is used which will load the correct constants based on arity and curve. Its possible to [generate](#constants) your own constants and load them. + +```rust +let ctx = get_default_device_context(); +let custom_poseidon = Poseidon::new( + arity, // The arity of poseidon hash. The width will be equal to arity + 1 + alpha, // The S-box power + full_rounds_half, + partial_rounds, + round_constants, + mds_matrix, + non_sparse_matrix, + sparse_matrices, + domain_tag, + ctx, +) +.unwrap(); +``` + +## The Tree Builder + +The tree builder allows you to build Merkle trees using Poseidon. + +You can define both the tree's `height` and its `arity`. The tree `height` determines the number of layers in the tree, including the root and the leaf layer. The `arity` determines how many children each internal node can have. + +```rust +use icicle_bn254::tree::Bn254TreeBuilder; +use icicle_bn254::poseidon::Poseidon; + +let mut config = TreeBuilderConfig::default(); +let arity = 2; +config.arity = arity as u32; +let input_block_len = arity; +let leaves = vec![F::one(); (1 << height) * arity]; +let mut digests = vec![F::zero(); merkle_tree_digests_len((height + 1) as u32, arity as u32, 1)]; + +let leaves_slice = HostSlice::from_slice(&leaves); +let digests_slice = HostSlice::from_mut_slice(&mut digests); + +let ctx = device_context::DeviceContext::default(); +let hash = Poseidon::load(2, &ctx).unwrap(); + +let mut config = TreeBuilderConfig::default(); +config.keep_rows = 5; +Bn254TreeBuilder::build_merkle_tree( + leaves_slice, + digests_slice, + height, + input_block_len, + &hash, + &hash, + &config, +) +.unwrap(); +``` + +Similar to Poseidon, you can also configure the Tree Builder `TreeBuilderConfig::default()` + +- `keep_rows`: The number of rows which will be written to output, 0 will write all rows. +- `are_inputs_on_device`: Have the inputs been loaded to device memory ? +- `is_async`: Should the TreeBuilder run asynchronously? `False` will block the current CPU thread. `True` will require you call `cudaStreamSynchronize` or `cudaDeviceSynchronize` to retrieve the result. + +### Benchmarks + +We ran the Poseidon tree builder on: + +**CPU**: 12th Gen Intel(R) Core(TM) i9-12900K/ + +**GPU**: RTX 3090 Ti + +**Tree height**: 30 (2^29 elements) + +The benchmarks include copying data from and to the device. + +| Rows to keep parameter | Run time, Icicle | Supranational PC2 +| ----------- | ----------- | ----------- +| 10 | 9.4 seconds | 13.6 seconds +| 20 | 9.5 seconds | 13.6 seconds +| 29 | 13.7 seconds | 13.6 seconds diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/poseidon2.md b/docs/versioned_docs/version-3.1.0/icicle/primitives/poseidon2.md new file mode 100644 index 000000000..622fc2a85 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/poseidon2.md @@ -0,0 +1,90 @@ +# Poseidon2 + +TODO update for V3 + +[Poseidon2](https://eprint.iacr.org/2023/323) is a recently released optimized version of Poseidon1. The two versions differ in two crucial points. First, Poseidon is a sponge hash function, while Poseidon2 can be either a sponge or a compression function depending on the use case. Secondly, Poseidon2 is instantiated by new and more efficient linear layers with respect to Poseidon. These changes decrease the number of multiplications in the linear layer by up to 90% and the number of constraints in Plonk circuits by up to 70%. This makes Poseidon2 currently the fastest arithmetization-oriented hash function without lookups. + + +## Using Poseidon2 + +ICICLE Poseidon2 is implemented for GPU and parallelization is performed for each state. +We calculate multiple hash-sums over multiple pre-images in parallel, rather than going block by block over the input vector. + +For example, for Poseidon2 of width 16, input rate 8, output elements 8 and input of size 1024 * 8, we would expect 1024 * 8 elements of output. Which means each input block would be of size 8, resulting in 1024 Poseidon2 hashes being performed. + +### Supported Bindings + +[`Rust`](https://github.com/ingonyama-zk/icicle/tree/main/wrappers/rust/icicle-core/src/poseidon2) + +### Constants + +Poseidon2 is also extremely customizable and using different constants will produce different hashes, security levels and performance results. + +We support pre-calculated constants for each of the [supported curves](../libraries#supported-curves-and-operations). The constants can be found [here](https://github.com/ingonyama-zk/icicle/tree/main/icicle/include/poseidon2/constants) and are labeled clearly per curve `_poseidon2.h`. + +You can also use your own set of constants as shown [here](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/rust/icicle-fields/icicle-babybear/src/poseidon2/mod.rs#L290) + +### Rust API + +This is the most basic way to use the Poseidon2 API. + +```rust +let test_size = 1 << 10; +let width = 16; +let rate = 8; +let ctx = get_default_device_context(); +let poseidon = Poseidon2::load(width, rate, MdsType::Default, DiffusionStrategy::Default, &ctx).unwrap(); +let config = HashConfig::default(); + +let inputs = vec![F::one(); test_size * rate as usize]; +let outputs = vec![F::zero(); test_size]; +let mut input_slice = HostOrDeviceSlice::on_host(inputs); +let mut output_slice = HostOrDeviceSlice::on_host(outputs); + +poseidon.hash_many::( + &mut input_slice, + &mut output_slice, + test_size as u32, + rate as u32, + 8, // Output length + &config, +) +.unwrap(); +``` + +In the example above `Poseidon2::load(width, rate, MdsType::Default, DiffusionStrategy::Default, &ctx).unwrap();` is used to load the correct constants based on width and curve. Here, the default MDS matrices and diffusion are used. If you want to get a Plonky3 compliant version, set them to `MdsType::Plonky` and `DiffusionStrategy::Montgomery` respectively. + +## The Tree Builder + +Similar to Poseidon1, you can use Poseidon2 in a tree builder. + +```rust +use icicle_bn254::tree::Bn254TreeBuilder; +use icicle_bn254::poseidon2::Poseidon2; + +let mut config = TreeBuilderConfig::default(); +let arity = 2; +config.arity = arity as u32; +let input_block_len = arity; +let leaves = vec![F::one(); (1 << height) * arity]; +let mut digests = vec![F::zero(); merkle_tree_digests_len((height + 1) as u32, arity as u32, 1)]; + +let leaves_slice = HostSlice::from_slice(&leaves); +let digests_slice = HostSlice::from_mut_slice(&mut digests); + +let ctx = device_context::DeviceContext::default(); +let hash = Poseidon2::load(arity, arity, MdsType::Default, DiffusionStrategy::Default, &ctx).unwrap(); + +let mut config = TreeBuilderConfig::default(); +config.keep_rows = 5; +Bn254TreeBuilder::build_merkle_tree( + leaves_slice, + digests_slice, + height, + input_block_len, + &hash, + &hash, + &config, +) +.unwrap(); +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/primitives/vec_ops.md b/docs/versioned_docs/version-3.1.0/icicle/primitives/vec_ops.md new file mode 100644 index 000000000..7f546dc16 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/primitives/vec_ops.md @@ -0,0 +1,214 @@ + +# Vector Operations API + +## Overview + +The Vector Operations API in Icicle provides a set of functions for performing element-wise and scalar-vector operations on vectors, matrix operations, and miscellaneous operations like bit-reversal and slicing. These operations can be performed on the host or device, with support for asynchronous execution. + +### VecOpsConfig + +The `VecOpsConfig` struct is a configuration object used to specify parameters for vector operations. + +#### Fields + +- **`stream: icicleStreamHandle`**: Specifies the CUDA stream for asynchronous execution. If `nullptr`, the default stream is used. +- **`is_a_on_device: bool`**: Indicates whether the first input vector (`a`) is already on the device. If `false`, the vector will be copied from the host to the device. +- **`is_b_on_device: bool`**: Indicates whether the second input vector (`b`) is already on the device. If `false`, the vector will be copied from the host to the device. This field is optional. +- **`is_result_on_device: bool`**: Indicates whether the result should be stored on the device. If `false`, the result will be transferred back to the host. +- **`is_async: bool`**: Specifies whether the vector operation should be performed asynchronously. When `true`, the operation will not block the CPU, allowing other operations to proceed concurrently. Asynchronous execution requires careful synchronization to ensure data integrity. +- **`batch_size: int`**: Number of vectors (or operations) to process in a batch. Each vector operation will be performed independently on each batch element. +- **`columns_batch: bool`**: True if the batched vectors are stored as columns in a 2D array (i.e., the vectors are strided in memory as columns of a matrix). If false, the batched vectors are stored contiguously in memory (e.g., as rows or in a flat array). +- **`ext: ConfigExtension*`**: Backend-specific extensions. + +#### Default Configuration + +```cpp +static VecOpsConfig default_vec_ops_config() { + VecOpsConfig config = { + nullptr, // stream + false, // is_a_on_device + false, // is_b_on_device + false, // is_result_on_device + false, // is_async + 1, // batch_size + false, // columns_batch + nullptr // ext + }; + return config; +} +``` + +### Element-wise Operations + +These functions perform element-wise operations on two input vectors a and b. If VecOpsConfig specifies a batch_size greater than one, the operations are performed on multiple pairs of vectors simultaneously, producing corresponding output vectors. + +#### `vector_add` + +Adds two vectors element-wise. + +```cpp +template +eIcicleError vector_add(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_sub` + +Subtracts vector `b` from vector `a` element-wise. + +```cpp +template +eIcicleError vector_sub(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_mul` + +Multiplies two vectors element-wise. + +```cpp +template +eIcicleError vector_mul(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_div` + +Divides vector `a` by vector `b` element-wise. + +```cpp +template +eIcicleError vector_div(const T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_accumulate` + +Adds vector b to a, inplace. + +```cpp +template +eIcicleError vector_accumulate(T* vec_a, const T* vec_b, uint64_t size, const VecOpsConfig& config); +``` + +#### `convert_montogomery` + +Convert a vector of field elements to/from montgomery form. +```cpp +template +eIcicleError convert_montgomery(const T* input, uint64_t size, bool is_into, const VecOpsConfig& config, T* output); +``` + +### Reduction operations + +These functions perform reduction operations on vectors. If VecOpsConfig specifies a batch_size greater than one, the operations are performed on multiple vectors simultaneously, producing corresponding output values. The storage arrangement of batched vectors is determined by the columns_batch field in the VecOpsConfig. + +#### `vector_sum` + +Computes the sum of all elements in each vector in a batch. + +```cpp +template +eIcicleError vector_sum(const T* vec_a, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `vector_product` + +Computes the product of all elements in each vector in a batch. + +```cpp +template +eIcicleError vector_product(const T* vec_a, uint64_t size, const VecOpsConfig& config, T* output); +``` + +### Scalar-Vector Operations + +These functions apply a scalar operation to each element of a vector. If VecOpsConfig specifies a batch_size greater than one, the operations are performed on multiple vector-scalar pairs simultaneously, producing corresponding output vectors. + +#### `scalar_add_vec / scalar_sub_vec` + +Adds a scalar to each element of a vector. + +```cpp +template +eIcicleError scalar_add_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `scalar_sub_vec` + +Subtract each element of a vector from a scalar `scalar-vec`. + +```cpp +template +eIcicleError scalar_sub_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +#### `scalar_mul_vec` + +Multiplies each element of a vector by a scalar. + +```cpp +template +eIcicleError scalar_mul_vec(const T* scalar_a, const T* vec_b, uint64_t size, const VecOpsConfig& config, T* output); +``` + +### Matrix Operations + +These functions perform operations on matrices. If VecOpsConfig specifies a batch_size greater than one, the operations are performed on multiple matrices simultaneously, producing corresponding output matrices. + +#### `matrix_transpose` + +Transposes a matrix. + +```cpp +template +eIcicleError matrix_transpose(const T* mat_in, uint32_t nof_rows, uint32_t nof_cols, const VecOpsConfig& config, T* mat_out); +``` + +### Miscellaneous Operations + +#### `bit_reverse` + +Reorders the vector elements based on a bit-reversal pattern. If VecOpsConfig specifies a batch_size greater than one, the operation is performed on multiple vectors simultaneously. + +```cpp +template +eIcicleError bit_reverse(const T* vec_in, uint64_t size, const VecOpsConfig& config, T* vec_out); +``` + +#### `slice` + +Extracts a slice from a vector. If VecOpsConfig specifies a batch_size greater than one, the operation is performed on multiple vectors simultaneously, producing corresponding output vectors. + +```cpp +template +eIcicleError slice(const T* vec_in, uint64_t offset, uint64_t stride, uint64_t size_in, uint64_t size_out, const VecOpsConfig& config, T* vec_out); +``` + +#### `highest_non_zero_idx` + +Finds the highest non-zero index in a vector. If VecOpsConfig specifies a batch_size greater than one, the operation is performed on multiple vectors simultaneously. + +```cpp +template +eIcicleError highest_non_zero_idx(const T* vec_in, uint64_t size, const VecOpsConfig& config, int64_t* out_idx); +``` + +#### `polynomial_eval` + +Evaluates a polynomial at given domain points. If VecOpsConfig specifies a batch_size greater than one, the operation is performed on multiple vectors simultaneously. + +```cpp +template +eIcicleError polynomial_eval(const T* coeffs, uint64_t coeffs_size, const T* domain, uint64_t domain_size, const VecOpsConfig& config, T* evals /*OUT*/); +``` + +#### `polynomial_division` + +Divides two polynomials. If VecOpsConfig specifies a batch_size greater than one, the operation is performed on multiple vectors simultaneously. + +```cpp +template +eIcicleError polynomial_division(const T* numerator, int64_t numerator_deg, const T* denumerator, int64_t denumerator_deg, const VecOpsConfig& config, T* q_out /*OUT*/, uint64_t q_size, T* r_out /*OUT*/, uint64_t r_size); +``` + +### Rust and Go bindings + +- [Golang](../golang-bindings/vec-ops.md) +- [Rust](../rust-bindings/vec-ops.md) diff --git a/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/cpp.md b/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/cpp.md new file mode 100644 index 000000000..b8f1f285f --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/cpp.md @@ -0,0 +1,300 @@ +# Icicle C++ Usage Guide + +## Overview + +This guide covers the usage of ICICLE's C++ API, including device management, memory operations, data transfer, synchronization, and compute APIs. + +## Device Management + +:::note +See all ICICLE runtime APIs in [runtime.h](https://github.com/ingonyama-zk/icicle/blob/main/icicle/include/icicle/runtime.h) +::: + +### Loading a Backend + +The backend can be loaded from a specific path or from an environment variable. This is essential for setting up the computing environment. + +```cpp +#include "icicle/runtime.h" +eIcicleError result = icicle_load_backend_from_env_or_default(); +// or load from custom install dir +eIcicleError result = icicle_load_backend("/path/to/backend/installdir", true); +``` + +### Setting and Getting Active Device + +You can set the active device for the current thread and retrieve it when needed: + +```cpp +icicle::Device device = {"CUDA", 0}; // or other +eIcicleError result = icicle_set_device(device); +// or query current (thread) device +eIcicleError result = icicle_get_active_device(device); +``` + +### Querying Device Information + +Retrieve the number of available devices and check if a pointer is allocated on the host or on the active device: + +```cpp +int device_count; +eIcicleError result = icicle_get_device_count(device_count); + +bool is_host_memory; +eIcicleError result = icicle_is_host_memory(ptr); + +bool is_device_memory; +eIcicleError result = icicle_is_active_device_memory(ptr); +``` + +## Memory Management + +### Allocating and Freeing Memory + +Memory can be allocated and freed on the active device: + +```cpp +void* ptr; +eIcicleError result = icicle_malloc(&ptr, 1024); // Allocate 1024 bytes +eIcicleError result = icicle_free(ptr); // Free the allocated memory +``` + +### Asynchronous Memory Operations + +You can perform memory allocation and deallocation asynchronously using streams: + +```cpp +icicleStreamHandle stream; +eIcicleError err = icicle_create_stream(&stream); + +void* ptr; +err = icicle_malloc_async(&ptr, 1024, stream); +err = icicle_free_async(ptr, stream); +``` + +### Querying Available Memory + +Retrieve the total and available memory on the active device: + +```cpp +size_t total_memory, available_memory; +eIcicleError err = icicle_get_available_memory(total_memory, available_memory); +``` + +### Setting Memory Values + +Set memory to a specific value on the active device, synchronously or asynchronously: + +```cpp +eIcicleError err = icicle_memset(ptr, 0, 1024); // Set 1024 bytes to 0 +eIcicleError err = icicle_memset_async(ptr, 0, 1024, stream); +``` + +## Data Transfer + +### Copying Data + +Data can be copied between host and device, or between devices. The location of the memory is inferred from the pointers: + +```cpp +eIcicleError result = icicle_copy(dst, src, size); +eIcicleError result = icicle_copy_async(dst, src, size, stream); +``` + +### Explicit Data Transfers + +To avoid device-inference overhead, use explicit copy functions: + +```cpp +eIcicleError result = icicle_copy_to_host(host_dst, device_src, size); +eIcicleError result = icicle_copy_to_host_async(host_dst, device_src, size, stream); + +eIcicleError result = icicle_copy_to_device(device_dst, host_src, size); +eIcicleError result = icicle_copy_to_device_async(device_dst, host_src, size, stream); +``` + +## Stream Management + +### Creating and Destroying Streams + +Streams are used to manage asynchronous operations: + +```cpp +icicleStreamHandle stream; +eIcicleError result = icicle_create_stream(&stream); +eIcicleError result = icicle_destroy_stream(stream); +``` + +## Synchronization + +### Synchronizing Streams and Devices + +Ensure all previous operations on a stream or device are completed before proceeding: + +```cpp +eIcicleError result = icicle_stream_synchronize(stream); +eIcicleError result = icicle_device_synchronize(); +``` + +## Device Properties + +### Checking Device Availability + +Check if a device is available and retrieve a list of registered devices: + +```cpp +icicle::Device dev; +eIcicleError result = icicle_is_device_available(dev); +``` + +### Querying Device Properties + +Retrieve properties of the active device: + +```cpp +DeviceProperties properties; +eIcicleError result = icicle_get_device_properties(properties); + +/******************/ +// where DeviceProperties is +struct DeviceProperties { + bool using_host_memory; // Indicates if the device uses host memory + int num_memory_regions; // Number of memory regions available on the device + bool supports_pinned_memory; // Indicates if the device supports pinned memory + // Add more properties as needed +}; +``` + + +## Compute APIs + +### Multi-Scalar Multiplication (MSM) Example + +Icicle provides high-performance compute APIs such as the Multi-Scalar Multiplication (MSM) for cryptographic operations. Here's a simple example of how to use the MSM API. + +```cpp +#include +#include "icicle/runtime.h" +#include "icicle/api/bn254.h" + +using namespace bn254; + +int main() +{ + // Load installed backends + icicle_load_backend_from_env_or_default(); + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + const bool is_cuda_device_available = (eIcicleError::SUCCESS == icicle_is_device_available("CUDA")); + if (is_cuda_device_available) { + Device device = {"CUDA", 0}; // GPU-0 + ICICLE_CHECK(icicle_set_device(device)); // ICICLE_CHECK asserts that the api call returns eIcicleError::SUCCESS + } // else we stay on CPU backend + + // Setup inputs + int msm_size = 1024; + auto scalars = std::make_unique(msm_size); + auto points = std::make_unique(msm_size); + projective_t result; + + // Generate random inputs + scalar_t::rand_host_many(scalars.get(), msm_size); + projective_t::rand_host_many(points.get(), msm_size); + + // (optional) copy scalars to device memory explicitly + scalar_t* scalars_d = nullptr; + auto err = icicle_malloc((void**)&scalars_d, sizeof(scalar_t) * msm_size); + // Note: need to test err and make sure no errors occurred + err = icicle_copy(scalars_d, scalars.get(), sizeof(scalar_t) * msm_size); + + // MSM configuration + MSMConfig config = default_msm_config(); + // tell icicle that the scalars are on device. Note that EC points and result are on host memory in this example. + config.are_scalars_on_device = true; + + // Execute the MSM kernel (on the current device) + eIcicleError result_code = msm(scalars_d, points.get(), msm_size, config, &result); + // OR call bn254_msm(scalars_d, points.get(), msm_size, config, &result); + + // Free the device memory + icicle_free(scalars_d); + + // Check for errors + if (result_code == eIcicleError::SUCCESS) { + std::cout << "MSM result: " << projective_t::to_affine(result) << std::endl; + } else { + std::cerr << "MSM computation failed with error: " << get_error_string(result_code) << std::endl; + } + + return 0; +} +``` + +### Polynomial Operations Example + +Here's another example demonstrating polynomial operations using Icicle: + +```cpp +#include +#include "icicle/runtime.h" +#include "icicle/polynomials/polynomials.h" +#include "icicle/api/bn254.h" + +using namespace bn254; + +// define bn254Poly to be a polynomial over the scalar field of bn254 +using bn254Poly = Polynomial; + +static bn254Poly randomize_polynomial(uint32_t size) +{ + auto coeff = std::make_unique(size); + for (int i = 0; i < size; i++) + coeff[i] = scalar_t::rand_host(); + return bn254Poly::from_rou_evaluations(coeff.get(), size); +} + +int main() +{ + // Load backend and set device + icicle_load_backend_from_env_or_default(); + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + const bool is_cuda_device_available = (eIcicleError::SUCCESS == icicle_is_device_available("CUDA")); + if (is_cuda_device_available) { + Device device = {"CUDA", 0}; // GPU-0 + ICICLE_CHECK(icicle_set_device(device)); // ICICLE_CHECK asserts that the API call returns eIcicleError::SUCCESS + } // else we stay on CPU backend + + int poly_size = 1024; + + // build domain for ntt is required for some polynomial ops that rely on ntt + ntt_init_domain(scalar_t::omega(12), default_ntt_init_domain_config()); + + // randomize polynomials f(x),g(x) over the scalar field of bn254 + bn254Poly f = randomize_polynomial(poly_size); + bn254Poly g = randomize_polynomial(poly_size); + + // Perform polynomial multiplication + auto result = f * g; // Executes on the current device + + ICICLE_LOG_INFO << "Done"; + + return 0; +} +``` + +In this example, the polynomial multiplication is used to perform polynomial multiplication on CUDA or CPU, showcasing the flexibility and power of Icicle's compute APIs. + +## Error Handling + +### Checking for Errors + +Icicle APIs return an `eIcicleError` enumeration value. Always check the returned value to ensure that operations were successful. + +```cpp +if (result != eIcicleError::SUCCESS) { + // Handle error +} +``` + +This guide provides an overview of the essential APIs available in Icicle for C++. The provided examples should help you get started with integrating Icicle into your high-performance computing projects. diff --git a/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/general.md b/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/general.md new file mode 100644 index 000000000..0bef2b850 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/general.md @@ -0,0 +1,112 @@ + +# Icicle Programmer's Guide + +## Compute APIs + +ICICLE offers a variety of compute APIs, including Number Theoretic Transforms (NTT), Multi Scalar Multiplication (MSM), vector operations, Elliptic Curve NTT (ECNTT), polynomials, and more. These APIs follow a consistent structure, making it straightforward to apply the same usage patterns across different operations. + +[Check out all details about compute APIs here](../primitives/overview.md). + +### Common Structure of Compute APIs + +Each compute API in Icicle typically involves the following components: + +- **Inputs and Outputs**: The data to be processed and the resulting output are passed to the API functions. These can reside either on the host (CPU) or on a device (GPU). + +- **Parameters**: Parameters such as the size of data to be processed are provided to control the computation. + +- **Configuration Struct**: A configuration struct is used to specify additional options for the computation. This struct has default values but can be customized as needed. + +The configuration struct allows users to modify settings such as: + +- Specifying whether inputs and outputs are on the host or device. +- Adjusting the data layout for specific optimizations. +- Setting batching parameters (batch_size and columns_batch) to perform operations on multiple data sets simultaneously. +- Passing custom options to the backend implementation through an extension mechanism, such as setting the number of CPU cores to use. + +### Example (C++) + +```cpp +#include "icicle/vec_ops.h" + +// Create config struct for vector add +VecOpsConfig config = default_vec_ops_config(); +// optionally modify the config struct here +config.batch_size = 4; // Process 4 vector operations in a batch +config.columns_batch = true; // Batched vectors are stored as columns + +// Call the API +eIcicleError err = vector_add(vec_a, vec_b, size, config, vec_res); +``` + +Where `VecOpsConfig` is defined in `icicle/vec_ops.h`: + +```cpp +struct VecOpsConfig { + icicleStreamHandle stream; /**< Stream for asynchronous execution. */ + bool is_a_on_device; /**< True if `a` is on the device, false if it is not. Default value: false. */ + bool is_b_on_device; /**< True if `b` is on the device, false if it is not. Default value: false. OPTIONAL. */ + bool is_result_on_device; /**< If true, the output is preserved on the device, otherwise on the host. Default value: false. */ + bool is_async; /**< Whether to run the vector operations asynchronously. */ + int batch_size; /**< Number of vector operations to process in a batch. Default value: 1. */ + bool columns_batch; /**< True if batched vectors are stored as columns; false if stored contiguously. Default value: false. */ + ConfigExtension* ext = nullptr; /**< Backend-specific extension. */ +}; +``` + +This pattern is consistent across most Icicle APIs, in C++/Rust/Go, providing flexibility while maintaining a familiar structure. For NTT, MSM, and other operations, include the corresponding header and call the template APIs. + +### Config struct extension + +In special cases, where an application wants to specify backend specific options, this is achieved with a config-extension struct. +For example the CPU backend has an option regarding how many threads to use for a vector addition looks as follows: +```cpp +#include "icicle/vec_ops.h" + +// Create config struct for vector add +VecOpsConfig config = default_vec_ops_config(); +ConfigExtension ext; +config.ext = &ext; +ext.set("n_threads", 8); // tell the CPU backend to use 8 threads +// Call the API +eIcicleError err = vector_add(vec_a, vec_b, size, config, vec_res); +``` + +:::note +This is not device-agnostic behavior, meaning such code is aware of the backend. +Having said that, it is not an error to pass options to a backend that is not aware of them. +::: + +## Device Abstraction + +ICICLE provides a device abstraction layer that allows you to interact with different compute devices such as CPUs and GPUs seamlessly. The device abstraction ensures that your code can work across multiple hardware platforms without modification. + +### Device Management + +- **Loading Backends**: Backends are loaded dynamically based on the environment configuration or a specified path. +- **Setting Active Device**: The active device for a thread can be set, allowing for targeted computation on a specific device. + +## Streams + +Streams in ICICLE allow for asynchronous execution and memory operations, enabling parallelism and non-blocking execution. Streams are associated with specific devices, and you can create, destroy, and synchronize streams to manage your workflow. + +:::note +For compute APIs, streams go into the `config.stream` field along with the `is_async=true` config flag. +::: + +### Memory Management + +Icicle provides functions for allocating, freeing, and managing memory across devices. Memory operations can be performed synchronously or asynchronously, depending on the use case. + +### Data Transfer + +Data transfer between the host and devices, or between different devices, is handled through a set of APIs that ensure efficient and error-checked operations. Asynchronous operations are supported to maximize performance. + +### Synchronization + +Synchronization ensures that all previous operations on a stream or device are completed. This is crucial when coordinating between multiple operations that depend on one another. + +## Additional Information + +- **Error Handling**: Icicle uses a specific error enumeration (`eIcicleError`) to handle and return error states across its APIs. +- **Device Properties**: You can query various properties of devices to tailor operations according to the hardware capabilities. diff --git a/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/go.md b/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/go.md new file mode 100644 index 000000000..92df226f9 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/go.md @@ -0,0 +1,294 @@ +# ICICLE Golang Usage Guide + +## Overview + +This guide covers the usage of ICICLE's Golang API, including device management, memory operations, data transfer, synchronization, and compute APIs. + +## Device Management + +:::note +See all ICICLE runtime APIs in [runtime.go](https://github.com/ingonyama-zk/icicle/blob/main/wrappers/golang/runtime/runtime.go) +::: + +### Loading a Backend + +The backend can be loaded from a specific path or from an environment variable. This is essential for setting up the computing environment. + +```go +import "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + +result := runtime.LoadBackendFromEnvOrDefault() +// or load from custom install dir +result := runtime.LoadBackend("/path/to/backend/installdir", true) +``` + +### Setting and Getting Active Device + +You can set the active device for the current thread and retrieve it when needed: + +```go +device = runtime.CreateDevice("CUDA", 0) // or other +result := runtime.SetDevice(device) +// or query current (thread) device +activeDevice := runtime.GetActiveDevice() +``` + +### Querying Device Information + +Retrieve the number of available devices and check if a pointer is allocated on the host or on the active device: + +```go +numDevices := runtime.GetDeviceCount() + +var ptr unsafe.Pointer +isHostMemory = runtime.IsHostMemory(ptr) +isDeviceMemory = runtime.IsActiveDeviceMemory(ptr) +``` + +## Memory Management + +### Allocating and Freeing Memory + +Memory can be allocated and freed on the active device: + +```go +ptr, err := runtime.Malloc(1024) // Allocate 1024 bytes +err := runtime.Free(ptr) // Free the allocated memory +``` + +### Asynchronous Memory Operations + +You can perform memory allocation and deallocation asynchronously using streams: + +```go +stream, err := runtime.CreateStream() + +ptr, err := runtime.MallocAsync(1024, stream) +err = runtime.FreeAsync(ptr, stream) +``` + +### Querying Available Memory + +Retrieve the total and available memory on the active device: + +```go +size_t total_memory, available_memory; +availableMemory, err := runtime.GetAvailableMemory() +freeMemory := availableMemory.Free +totalMemory := availableMemory.Total +``` + +### Setting Memory Values + +Set memory to a specific value on the active device, synchronously or asynchronously: + +```go +err := runtime.Memset(ptr, 0, 1024) // Set 1024 bytes to 0 +err := runtime.MemsetAsync(ptr, 0, 1024, stream) +``` + +## Data Transfer + +### Explicit Data Transfers + +To avoid device-inference overhead, use explicit copy functions: + +```go +result := runtime.CopyToHost(host_dst, device_src, size) +result := runtime.CopyToHostAsync(host_dst, device_src, size, stream) +result := runtime.CopyToDevice(device_dst, host_src, size) +result := runtime.CopyToDeviceAsync(device_dst, host_src, size, stream) +``` + +## Stream Management + +### Creating and Destroying Streams + +Streams are used to manage asynchronous operations: + +```go +stream, err := runtime.CreateStream() +err = runtime.DestroyStream(stream) +``` + +## Synchronization + +### Synchronizing Streams and Devices + +Ensure all previous operations on a stream or device are completed before proceeding: + +```go +err := runtime.StreamSynchronize(stream) +err := runtime.DeviceSynchronize() +``` + +## Device Properties + +### Checking Device Availability + +Check if a device is available and retrieve a list of registered devices: + +```go +dev := runtime.CreateDevice("CPU", 0) +isCPUAvail := runtime.IsDeviceAvailable(dev) +``` + +### Querying Device Properties + +Retrieve properties of the active device: + +```go +properties, err := runtime.GetDeviceProperties(properties); + +/******************/ +// where DeviceProperties is +type DeviceProperties struct { + UsingHostMemory bool // Indicates if the device uses host memory + NumMemoryRegions int32 // Number of memory regions available on the device + SupportsPinnedMemory bool // Indicates if the device supports pinned memory +} +``` + +## Compute APIs + +### Multi-Scalar Multiplication (MSM) Example + +Icicle provides high-performance compute APIs such as the Multi-Scalar Multiplication (MSM) for cryptographic operations. Here's a simple example of how to use the MSM API. + +```go +package main + +import ( + "fmt" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254" + bn254Msm "github.com/ingonyama-zk/icicle/v3/wrappers/golang/curves/bn254/msm" +) + +func main() { + + // Load installed backends + runtime.LoadBackendFromEnvOrDefault() + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + deviceCuda := runtime.CreateDevice("CUDA", 0) // GPU-0 + if runtime.IsDeviceAvailable(&deviceCuda) { + runtime.SetDevice(&deviceCuda) + } // else we stay on CPU backend + + // Setup inputs + const size = 1 << 18 + + // Generate random inputs + scalars := bn254.GenerateScalars(size) + points := bn254.GenerateAffinePoints(size) + + // (optional) copy scalars to device memory explicitly + var scalarsDevice core.DeviceSlice + scalars.CopyToDevice(&scalarsDevice, true) + + // MSM configuration + cfgBn254 := core.GetDefaultMSMConfig() + + // allocate memory for the result + result := make(core.HostSlice[bn254.Projective], 1) + + // execute bn254 MSM on device + err := bn254Msm.Msm(scalarsDevice, points, &cfgBn254, result) + + // Check for errors + if err != runtime.Success { + errorString := fmt.Sprint( + "bn254 Msm failed: ", err) + panic(errorString) + } + + // free explicitly allocated device memory + scalarsDevice.Free() +} +``` + +### Polynomial Operations Example + +Here's another example demonstrating polynomial operations using Icicle: + +```go +package main + +import ( + "fmt" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/core" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/runtime" + + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear/ntt" + "github.com/ingonyama-zk/icicle/v3/wrappers/golang/fields/babybear/polynomial" +) + +func initBabybearDomain() runtime.EIcicleError { + cfgInitDomain := core.GetDefaultNTTInitDomainConfig() + rouIcicle := babybear.ScalarField{} + rouIcicle.FromUint32(1461624142) + return ntt.InitDomain(rouIcicle, cfgInitDomain) +} + +func init() { + // Load installed backends + runtime.LoadBackendFromEnvOrDefault() + + // trying to choose CUDA if available, or fallback to CPU otherwise (default device) + deviceCuda := runtime.CreateDevice("CUDA", 0) // GPU-0 + if runtime.IsDeviceAvailable(&deviceCuda) { + runtime.SetDevice(&deviceCuda) + } // else we stay on CPU backend + + // build domain for ntt is required for some polynomial ops that rely on ntt + err := initBabybearDomain() + if err != runtime.Success { + errorString := fmt.Sprint( + "Babybear Domain initialization failed: ", err) + panic(errorString) + } +} + +func main() { + + // Setup inputs + const polySize = 1 << 10 + + // randomize two polynomials over babybear field + var fBabybear polynomial.DensePolynomial + defer fBabybear.Delete() + var gBabybear polynomial.DensePolynomial + defer gBabybear.Delete() + fBabybear.CreateFromCoeffecitients(babybear.GenerateScalars(polySize)) + gBabybear.CreateFromCoeffecitients(babybear.GenerateScalars(polySize / 2)) + + // Perform polynomial multiplication + rBabybear := fBabybear.Multiply(&gBabybear) // Executes on the current device + defer rBabybear.Delete() + rDegree := rBabybear.Degree() + + fmt.Println("f Degree: ", fBabybear.Degree()) + fmt.Println("g Degree: ", gBabybear.Degree()) + fmt.Println("r Degree: ", rDegree) +} +``` + +In this example, the polynomial multiplication is used to perform polynomial multiplication on CUDA or CPU, showcasing the flexibility and power of Icicle's compute APIs. + +## Error Handling + +### Checking for Errors + +Icicle APIs return an `EIcicleError` enumeration value. Always check the returned value to ensure that operations were successful. + +```go +if result != runtime.SUCCESS { + // Handle error +} +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/rust.md b/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/rust.md new file mode 100644 index 000000000..af5caa5a8 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/programmers_guide/rust.md @@ -0,0 +1,247 @@ + +# Icicle Rust Usage Guide + +## Overview + +This guide covers the usage of ICICLE’s Rust API, including device management, memory operations, data transfer, synchronization, and compute APIs. + +## Build the Rust Application and Execute + +To successfully build and execute the Rust application using ICICLE, you need to define the ICICLE dependencies in your Cargo.toml file: + +```bash +[dependencies] +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +icicle-babybear = { git = "https://github.com/ingonyama-zk/icicle.git", branch="main" } +# add other ICICLE crates here as needed +``` + +Once the dependencies are defined, you can build and run your application using the following command: +```bash +cargo run --release +``` + +This will compile your Rust application with optimizations and execute it. + +:::note +The icicle-runtime crate is used to load backends, select a device, and interact with the device in an abstract way when managing memory, streams, and other resources, as explained in this guide. +::: + +## Device Management + +### Loading a Backend + +The backend can be loaded from a specific path or from an environment variable. This is essential for setting up the computing environment. + +```rust +use icicle_runtime::runtime; + +runtime::load_backend_from_env_or_default().unwrap(); +// or load from custom install dir +runtime::load_backend("/path/to/backend/installdir").unwrap(); +``` + +### Setting and Getting Active Device + +You can set the active device for the current thread and retrieve it when needed: + +```rust +use icicle_runtime::Device; + +let device = Device::new("CUDA", 0); // or other +icicle_runtime::set_device(&device).unwrap(); + +let active_device = icicle_runtime::get_active_device().unwrap(); +``` + +### Querying Device Information + +Retrieve the number of available devices and check if a pointer is allocated on the host or on the active device: + +```rust +let device_count = icicle_runtime::get_device_count().unwrap(); +``` + +## Memory Management + +### Allocating and Freeing Memory + +Memory can be allocated on the active device using the `DeviceVec` API. This memory allocation is flexible, as it supports allocation on any device, including the CPU if the CPU backend is used. + +```rust +use icicle_runtime::memory::DeviceVec; + +// Allocate 1024 elements on the device +let mut device_memory: DeviceVec = DeviceVec::::device_malloc(1024).unwrap(); +``` + +The memory is released when the `DeviceVec` object is dropped. + +### Asynchronous Memory Operations + +Asynchronous memory operations can be performed using streams. This allows for non-blocking execution, with memory allocation and deallocation occurring asynchronously. +```rust +use icicle_runtime::stream::IcicleStream; +use icicle_runtime::memory::DeviceVec; + +let mut stream = IcicleStream::create().unwrap(); // mutability is for the destroy() method + +// Allocate 1024 elements asynchronously on the device +let mut device_memory: DeviceVec = DeviceVec::::device_malloc_async(1024, &stream).unwrap(); + +// dispatch additional copy, compute etc. ops to the stream + +// Synchronize the stream to ensure all operations are complete +stream.synchronize().unwrap(); +stream.destroy().unwrap(); // +``` + +:::note +Streams need be explicitly destroyed before being dropped. +::: + +### Querying Available Memory + +You can retrieve the total and available memory on the active device using the `get_available_memory` function. + +```rust +use icicle_runtime::memory::get_available_memory; + +// Retrieve total and available memory on the active device +let (total_memory, available_memory) = get_available_memory().unwrap(); + +println!("Total memory: {}", total_memory); +println!("Available memory: {}", available_memory); +``` + +This function returns a tuple containing the total memory and the currently available memory on the device. It is essential for managing and optimizing resource usage in your applications. + +## Data Transfer + +### Copying Data + +Data can be copied between the host and device, or between devices. The location of the memory is handled by the `HostOrDeviceSlice` and `DeviceSlice` traits: + +```rust +use icicle_runtime::memory::{DeviceVec, HostSlice}; + +// Copy data from host to device +let input = vec![1, 2, 3, 4]; +let mut d_mem = DeviceVec::::device_malloc(input.len()).unwrap(); +d_mem.copy_from_host(HostSlice::from_slice(&input)).unwrap(); +// OR +d_mem.copy_from_host_async(HostSlice::from_slice(&input, &stream)).unwrap(); + +// Copy data back from device to host +let mut output = vec![0; input.len()]; +d_mem.copy_to_host(HostSlice::from_mut_slice(&mut output)).unwrap(); +// OR +d_mem.copy_to_host_async(HostSlice::from_mut_slice(&mut output, &stream)).unwrap(); +``` +## Stream Management + +### Creating and Destroying Streams + +Streams in Icicle are used to manage asynchronous operations, ensuring that computations can run in parallel without blocking the CPU thread: + +```rust +use icicle_runtime::stream::IcicleStream; + +// Create a stream +let mut stream = IcicleStream::create().unwrap(); + +// Destroy the stream +stream.destroy().unwrap(); +``` + +## Synchronization + +### Synchronizing Streams and Devices + +Synchronization ensures that all previous operations on a stream or device are completed before moving on to the next task. This is crucial when coordinating between multiple dependent operations: + +```rust +use icicle_runtime::stream::IcicleStream; + +// Synchronize the stream +stream.synchronize().unwrap(); + +// Synchronize the device +icicle_runtime::device_synchronize().unwrap(); +``` +These functions ensure that your operations are properly ordered and completed before the program proceeds, which is critical in parallel computing environments. + +## Device Properties + +### Checking Device Availability + +Check if a specific device is available and retrieve a list of registered devices: +```rust +use icicle_runtime::Device; + +let cuda_device = Device::new("CUDA", 0); +if icicle_runtime::is_device_available(&cuda_device) { + println!("CUDA device is available."); +} else { + println!("CUDA device is not available."); +} + +let registered_devices = icicle_runtime::get_registered_devices().unwrap(); +println!("Registered devices: {:?}", registered_devices); +``` + +### Querying Device Properties + +Retrieve properties of the active device to understand its capabilities and configurations: + +```rust +use icicle_runtime::Device; + +let cuda_device = Device::new("CUDA", 0); +if icicle_runtime::is_device_available(&cuda_device) { + icicle_runtime::set_device(&cuda_device); + let device_props = icicle_runtime::get_device_properties().unwrap(); + println!("Device using host memory: {}", device_props.using_host_memory); +} +``` + +These functions allow you to query device capabilities and ensure that your application is running on the appropriate hardware. + +## Compute APIs + +### Multi-Scalar Multiplication (MSM) Example + +Icicle provides high-performance compute APIs such as Multi-Scalar Multiplication (MSM) for cryptographic operations. Here's a simple example of how to use the MSM API in Rust. + +```rust +// Using bls12-377 curve +use icicle_bls12_377::curve::{CurveCfg, G1Projective, ScalarCfg}; +use icicle_core::{curve::Curve, msm, msm::MSMConfig, traits::GenerateRandom}; +use icicle_runtime::{device::Device, memory::HostSlice}; + +fn main() { + // Load backend and set device + let _ = icicle_runtime::runtime::load_backend_from_env_or_default(); + let cuda_device = Device::new("CUDA", 0); + if icicle_runtime::is_device_available(&cuda_device) { + icicle_runtime::set_device(&cuda_device).unwrap(); + } + + let size = 1024; + + // Randomize inputs + let points = CurveCfg::generate_random_affine_points(size); + let scalars = ScalarCfg::generate_random(size); + + let mut msm_results = vec![G1Projective::zero(); 1]; + msm::msm( + HostSlice::from_slice(&scalars), + HostSlice::from_slice(&points), + &MSMConfig::default(), + HostSlice::from_mut_slice(&mut msm_results[..]), + ) + .unwrap(); + println!("MSM result = {:?}", msm_results); +} +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/rust-bindings.md b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings.md new file mode 100644 index 000000000..ac5915b7a --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings.md @@ -0,0 +1,34 @@ +# Rust bindings + +Rust bindings allow you to use ICICLE as a rust library. + +`icicle-core` defines all interfaces, macros and common methods. + +`icicle-runtime` contains runtime APIs for memory management, stream management and more. + +`icicle-curves` / `icicle-fields` implement all interfaces and macros from icicle-core for each curve. For example icicle-bn254 implements curve bn254. Each curve has its own build script which will build the ICICLE libraries for that curve as part of the rust-toolchain build. + +## Using ICICLE Rust bindings in your project + +Simply add the following to your `Cargo.toml`. + +```toml +# GPU Icicle integration +icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle.git" } +icicle-core = { git = "https://github.com/ingonyama-zk/icicle.git" } +icicle-bn254 = { git = "https://github.com/ingonyama-zk/icicle.git" } +``` + +`icicle-bn254` being the curve you wish to use and `icicle-core` and `icicle-runtime` contain ICICLE utilities and CUDA wrappers. + +If you wish to point to a specific ICICLE branch add `branch = ""` or `tag = ""` to the ICICLE dependency. For a specific commit add `rev = ""`. + +When you build your project ICICLE will be built as part of the build command. + +## How do the rust bindings work? + +The rust bindings are rust crates that wrap the ICICLE Core libraries (C++). Each crate can wrap one or more ICICLE core libraries. They are built too when building the crate. + +:::note +Since ICICLE V3, core libraries are shared-libraries. This means that they must be installed in a directory that can be found by the linker. In addition, installing an application that depends on ICICLE must make sure to install ICICLE or have it installed on the target machine. +::: diff --git a/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/ecntt.md b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/ecntt.md new file mode 100644 index 000000000..426324a93 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/ecntt.md @@ -0,0 +1,25 @@ +# ECNTT + +## ECNTT Method + +The `ecntt` function computes the Elliptic Curve Number Theoretic Transform (EC-NTT) or its inverse on a batch of points of a curve. + +```rust +pub fn ecntt( + input: &(impl HostOrDeviceSlice> + ?Sized), + dir: NTTDir, + cfg: &NTTConfig, + output: &mut (impl HostOrDeviceSlice> + ?Sized), +) -> Result<(), eIcicleError> +``` + +## Parameters + +- **`input`**: The input data as a slice of `Projective`. This represents points on a specific elliptic curve `C`. +- **`dir`**: The direction of the NTT. It can be `NTTDir::kForward` for forward NTT or `NTTDir::kInverse` for inverse NTT. +- **`cfg`**: The NTT configuration object of type `NTTConfig`. This object specifies parameters for the NTT computation, such as the batch size and algorithm to use. +- **`output`**: The output buffer to write the results into. This should be a slice of `Projective` with the same size as the input. + +## Return Value + +- **`Result<(), eIcicleError>`**: This function returns an `eIcicleError` which is a wrapper type that indicates success or failure of the NTT computation. On success, it contains `Ok(())`. diff --git a/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/hash.md b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/hash.md new file mode 100644 index 000000000..b57c76fe8 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/hash.md @@ -0,0 +1,110 @@ +# ICICLE Hashing in Rust + +:::note +For a general overview of ICICLE's hashing logic and supported algorithms, check out the [ICICLE Hashing Overview](../primitives/hash.md). +::: + +## Overview + +The ICICLE library provides Rust bindings for hashing using a variety of cryptographic hash functions. These hash functions are optimized for both general-purpose data and cryptographic operations such as multi-scalar multiplication, commitment generation, and Merkle tree construction. + +This guide will show you how to use the ICICLE hashing API in Rust with examples for common hash algorithms, such as Keccak-256, Keccak-512, SHA3-256, SHA3-512, Blake2s, and Poseidon. + +## Importing Hash Functions + +To use the hashing functions in Rust, you will need to import the specific hash algorithm module from the ICICLE Rust bindings. For example: + +```rust +use icicle_hash::keccak::Keccak256; +use icicle_hash::keccak::Keccak512; +use icicle_hash::sha3::Sha3_256; +use icicle_hash::sha3::Sha3_512; +use icicle_hash::blake2s::Blake2s; +use icicle_core::poseidon::Poseidon; +``` + +## API Usage + +### 1. Creating a Hasher Instance + +Each hash algorithm can be instantiated by calling its respective constructor. The new function takes an optional default input size, which can be set to 0 unless required for a specific use case. + +Example for Keccak-256: + +```rust +let keccak_hasher = Keccak256::new(0 /* default input size */).unwrap(); +``` + +### 2. Hashing a Simple String + +Once you have created a hasher instance, you can hash any input data, such as strings or byte arrays, and store the result in an output buffer. +Here’s how to hash a simple string using Keccak-256: + +```rust +use icicle_hash::keccak::Keccak256; +use icicle_runtime::memory::HostSlice; +use icicle_core::hash::HashConfig; +use hex; + +let input_str = "I like ICICLE! it's so fast and easy"; +let mut output = vec![0u8; 32]; // 32-byte output buffer + +let keccak_hasher = Keccak256::new(0 /* default input size */).unwrap(); +keccak_hasher + .hash( + HostSlice::from_slice(input_str.as_bytes()), // Input data + &HashConfig::default(), // Default configuration + HostSlice::from_mut_slice(&mut output), // Output buffer + ) + .unwrap(); + +// convert the output to a hex string for easy readability +let output_as_hex_str = hex::encode(output); +println!("Hash(`{}`) = {:?}", input_str, &output_as_hex_str); + +``` + +### 3. Poseidon Example (field elements) and batch hashing + +The Poseidon hash is designed for cryptographic field elements and curves, making it ideal for use cases such as zero-knowledge proofs (ZKPs). +Poseidon hash using babybear field: + +```rust +use icicle_babybear::field::{ScalarCfg, ScalarField}; +use icicle_core::hash::HashConfig; +use icicle_core::poseidon::{Poseidon, PoseidonHasher}; +use icicle_core::traits::FieldImpl; +use icicle_runtime::memory::HostSlice; + +let batch = 1 << 10; // Number of hashes to compute in a single batch +let t = 3; // Poseidon parameter that specifies the arity (number of inputs) for each hash function +let mut outputs = vec![ScalarField::zero(); batch]; // Output array sized for the batch count + +// Case (1): Hashing without a domain tag +// Generates 'batch * t' random input elements as each hash needs 't' inputs +let inputs = ScalarCfg::generate_random(batch * t); +let poseidon_hasher = Poseidon::new::(t as u32, None /*=domain-tag*/).unwrap(); // Instantiate Poseidon without domain tag + +poseidon_hasher + .hash( + HostSlice::from_slice(&inputs), // Input slice for the hash function + &HashConfig::default(), // Default hashing configuration + HostSlice::from_mut_slice(&mut outputs), // Output slice to store hash results + ) + .unwrap(); + +// Case (2): Hashing with a domain tag +// Generates 'batch * (t - 1)' inputs, as domain tag counts as one input in each hash +let inputs = ScalarCfg::generate_random(batch * (t - 1)); +let domain_tag = ScalarField::zero(); // Example domain tag (can be any valid field element) +let poseidon_hasher_with_domain_tag = Poseidon::new::(t as u32, Some(&domain_tag) /*=domain-tag*/).unwrap(); + +poseidon_hasher_with_domain_tag + .hash( + HostSlice::from_slice(&inputs), // Input slice with 't - 1' elements per hash + &HashConfig::default(), // Default hashing configuration + HostSlice::from_mut_slice(&mut outputs), // Output slice to store hash results + ) + .unwrap(); +``` + diff --git a/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/merkle.md b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/merkle.md new file mode 100644 index 000000000..40910a038 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/merkle.md @@ -0,0 +1,281 @@ + +# Merkle Tree API Documentation (Rust) + +This is the Rust version of the **Merkle Tree API Documentation** ([C++ documentation](../primitives/merkle.md)). It mirrors the structure and functionality of the C++ version, providing equivalent APIs in Rust. +For more detailed explanations, refer to the [C++ documentation](../primitives/merkle.md). + +To see a complete implementation, visit the [Hash and Merkle example](https://github.com/ingonyama-zk/icicle/tree/main/examples/rust/hash-and-merkle) for a full example. + +## Tree Structure and Configuration in Rust + +### Defining a Merkle Tree + +```rust +struct MerkleTree{ + /// * `layer_hashers` - A vector of hash objects representing the hashers of each layer. + /// * `leaf_element_size` - Size of each leaf element. + /// * `output_store_min_layer` - Minimum layer at which the output is stored. + /// + /// # Returns a new `MerkleTree` instance or eIcicleError. + pub fn new( + layer_hashers: &[&Hasher], + leaf_element_size: u64, + output_store_min_layer: u64, + ) -> Result; +} +``` + +The `output_store_min_layer` parameter defines the lowest layer that will be stored in memory. Layers below this value will not be stored, saving memory at the cost of additional computation when proofs are generated. + + + +### Building the Tree + +The Merkle tree can be constructed from input data of any type, allowing flexibility in its usage. The size of the input must align with the tree structure defined by the hash layers and leaf size. If the input size does not match the expected size, padding may be applied. + +Refer to the [Padding Section](#padding) for more details on how mismatched input sizes are handled. + +```rust +struct MerkleTree{ + /// * `leaves` - A slice of leaves (input data). + /// * `config` - Configuration for the Merkle tree. + /// + /// # Returns a result indicating success or failure. + pub fn build( + &self, + leaves: &(impl HostOrDeviceSlice + ?Sized), + cfg: &MerkleTreeConfig, + ) -> Result<(), eIcicleError>; +} +``` + + + +## Tree Examples in Rust + +### Example A: Binary Tree + +A binary tree with **5 layers**, using **Keccak-256**: + +![Merkle Tree Diagram](../primitives/merkle_diagrams/diagram1.png) + +```rust +use icicle_core::{ + hash::{HashConfig, Hasher}, + merkle::{MerkleTree, MerkleTreeConfig}, +}; +use icicle_hash::keccak::Keccak256; +use icicle_runtime::memory::HostSlice; + +let leaf_size = 1024_u64; +let max_input_size = leaf_size as usize * 16; +let input: Vec = vec![0; max_input_size]; + +// define layer hashers +// we want one hash layer to hash every 1KB to 32B then compress every 64B so 4 more binary layers +let hash = Keccak256::new(leaf_size).unwrap(); +let compress = Keccak256::new(2 * hash.output_size()).unwrap(); +let _layer_hashers = vec![&hash, &compress, &compress, &compress, &compress]; +// or like that +let layer_hashers: Vec<&Hasher> = std::iter::once(&hash) + .chain(std::iter::repeat(&compress).take(4)) + .collect(); + +let merkle_tree = MerkleTree::new(&layer_hashers, leaf_size, 0 /*min layer to store */).unwrap(); + +// compute the tree +merkle_tree + .build(HostSlice::from_slice(&input), &MerkleTreeConfig::default()) + .unwrap(); +``` + + + +### Example B: Tree with Arity 4 + +![Merkle Tree Diagram](../primitives/merkle_diagrams/diagram2.png) + +This example uses **Blake2s** in upper layers: + +```rust +use icicle_hash::blake2s::Blake2s; + +// define layer hashers +// we want one hash layer to hash every 1KB to 32B then compress every 128B so only 2 more layers +let hash = Keccak256::new(leaf_size).unwrap(); +let compress = Blake2s::new(4 * hash.output_size()).unwrap(); +let layer_hashers = vec![&hash, &compress, &compress]; + +let merkle_tree = MerkleTree::new(&layer_hashers, leaf_size, 0 /*min layer to store */).unwrap(); + +merkle_tree + .build(HostSlice::from_slice(&input), &MerkleTreeConfig::default()) + .unwrap(); +``` + + + +## Padding + +:::note +Padding feature is not yet supported in **v3.1** and is planned for **v3.2**. +::: + +When the input for **layer 0** is smaller than expected, ICICLE can apply **padding** to align the data. + +**Padding Schemes:** +1. **Zero padding:** Adds zeroes to the remaining space. +2. **Repeat last leaf:** The final leaf element is repeated to fill the remaining space. + +```rust +// pub enum PaddingPolicy { +// None, // No padding, assume input is correctly sized. +// ZeroPadding, // Pad the input with zeroes to fit the expected input size. +// LastValue, // Pad the input by repeating the last value. +// } + +use icicle_core::merkle::PaddingPolicy; +let mut config = MerkleTreeConfig::default(); +config.padding_policy = PaddingPolicy::ZeroPadding; +merkle_tree + .build(HostSlice::from_slice(&input), &config) + .unwrap(); +``` + + + +## Root as Commitment + +Retrieve the Merkle-root and serialize. + +```rust +struct MerkleTree{ + /// Retrieve the root of the Merkle tree. + /// + /// # Returns + /// A reference to the root hash. + pub fn get_root(&self) -> Result<&[T], eIcicleError>; +} + +let commitment: &[u8] = merkle_tree + .get_root() + .unwrap(); +println!("Commitment: {:?}", commitment);**** +``` + +:::warning +The commitment can be serialized to the proof. This is not handled by ICICLE. +::: + + + +## Generating Merkle Proofs + +Merkle proofs are used to **prove the integrity of opened leaves** in a Merkle tree. A proof ensures that a specific leaf belongs to the committed data by enabling the verifier to reconstruct the **root hash (commitment)**. + +A Merkle proof contains: + +- **Leaf**: The data being verified. +- **Index** (leaf_idx): The position of the leaf in the original dataset. +- **Path**: A sequence of sibling hashes (tree nodes) needed to recompute the path from the leaf to the root. + +![Merkle Pruned Phat Diagram](../primitives/merkle_diagrams/diagram1_path.png) + +```rust +struct MerkleTree{ + /// * `leaves` - A slice of leaves (input data). + /// * `leaf_idx` - Index of the leaf to generate a proof for. + /// * `pruned_path` - Whether the proof should be pruned. + /// * `config` - Configuration for the Merkle tree. + /// + /// # Returns a `MerkleProof` object or eIcicleError + pub fn get_proof( + &self, + leaves: &(impl HostOrDeviceSlice + ?Sized), + leaf_idx: u64, + pruned_path: bool, + config: &MerkleTreeConfig, + ) -> Result; +} +``` + +### Example: Generating a Proof + +Generating a proof for leaf idx 5: + +```rust +let merkle_proof = merkle_tree + .get_proof( + HostSlice::from_slice(&input), + 5, /*=leaf-idx*/ + true, /*pruned*/ + &MerkleTreeConfig::default(), + ) + .unwrap(); +``` + +:::warning +The Merkle-path can be serialized to the proof along with the leaf. This is not handled by ICICLE. +::: + + + +## Verifying Merkle Proofs + +```rust +struct MerkleTree{ + /// * `proof` - The Merkle proof to verify. + /// + /// # Returns a result indicating whether the proof is valid. + pub fn verify(&self, proof: &MerkleProof) -> Result; +} +``` + +### Example: Verifying a Proof + +```rust +let valid = merkle_tree + .verify(&merkle_proof) + .unwrap(); +assert!(valid); +``` + + + +## Pruned vs. Full Merkle-paths + +A **Merkle path** is a collection of **sibling hashes** that allows the verifier to **reconstruct the root hash** from a specific leaf. +This enables anyone with the **path and root** to verify that the **leaf** belongs to the committed dataset. +There are two types of paths that can be computed: + +- [**Pruned Path:**](#generating-merkle-proofs) Contains only necessary sibling hashes. +- **Full Path:** Contains all sibling nodes and intermediate hashes. + +![Merkle Full Path Diagram](../primitives//merkle_diagrams/diagram1_path_full.png) + +To compute a full path, specify `pruned=false`: + +```rust +let merkle_proof = merkle_tree + .get_proof( + HostSlice::from_slice(&input), + 5, /*=leaf-idx*/ + false, /*non-pruned is a full path --> note the pruned flag here*/ + &MerkleTreeConfig::default(), + ) + .unwrap(); +``` + + + +## Handling Partial Tree Storage + +In cases where the **Merkle tree is large**, only the **top layers** may be stored to conserve memory. +When opening leaves, the **first layers** (closest to the leaves) are **recomputed dynamically**. + +For example to avoid storing first layer we can define a tree as follows: + + +```rust +let mut merkle_tree = MerkleTree::new(&layer_hashers, leaf_size, 1 /*min layer to store*/); +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/msm.md b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/msm.md new file mode 100644 index 000000000..e4f86444f --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/msm.md @@ -0,0 +1,117 @@ +# MSM + +## MSM API Overview + +```rust +pub fn msm>( + scalars: &(impl HostOrDeviceSlice + ?Sized), + bases: &(impl HostOrDeviceSlice> + ?Sized), + cfg: &MSMConfig, + results: &mut (impl HostOrDeviceSlice> + ?Sized), +) -> Result<(), eIcicleError>; +``` + +### Parameters + +- **`scalars`**: A buffer containing the scalar values to be multiplied with corresponding points. +- **`points`**: A buffer containing the points to be multiplied by the scalars. +- **`cfg`**: MSM configuration specifying additional parameters for the operation. +- **`results`**: A buffer where the results of the MSM operations will be stored. + +### MSM Config + +```rust +pub struct MSMConfig { + pub stream_handle: IcicleStreamHandle, + pub precompute_factor: i32, + pub c: i32, + pub bitsize: i32, + batch_size: i32, + are_points_shared_in_batch: bool, + are_scalars_on_device: bool, + pub are_scalars_montgomery_form: bool, + are_points_on_device: bool, + pub are_points_montgomery_form: bool, + are_results_on_device: bool, + pub is_async: bool, + pub ext: ConfigExtension, +} +``` + +- **`stream_handle: IcicleStreamHandle`**: Specifies a stream for asynchronous execution. +- **`precompute_factor: i32`**: Determines the number of extra points to pre-compute for each point, affecting memory footprint and performance. +- **`c: i32`**: The "window bitsize," a parameter controlling the computational complexity and memory footprint of the MSM operation. +- **`bitsize: i32`**: The number of bits of the largest scalar, typically equal to the bit size of the scalar field. +- **`batch_size: i32`**: The number of MSMs to compute in a single batch, for leveraging parallelism. +- **`are_scalars_montgomery_form`**: Set to `true` if scalars are in montgomery form. +- **`are_points_montgomery_form`**: Set to `true` if points are in montgomery form. +- **`are_scalars_on_device: bool`**, **`are_points_on_device: bool`**, **`are_results_on_device: bool`**: Indicate whether the corresponding buffers are on the device memory. +- **`is_async: bool`**: Whether to perform the MSM operation asynchronously. +- **`ext: ConfigExtension`**: extended configuration for backend. + +### Usage + +The `msm` function is designed to compute the sum of multiple scalar-point multiplications efficiently. It supports both single MSM operations and batched operations for increased performance. The configuration allows for detailed control over the execution environment and performance characteristics of the MSM operation. + +When performing MSM operations, it's crucial to match the size of the `scalars` and `points` arrays correctly and ensure that the `results` buffer is appropriately sized to hold the output. The `MSMConfig` should be set up to reflect the specifics of the operation, including whether the operation should be asynchronous and any device-specific settings. + +## Example + +```rust +// Using bls12-377 curve +use icicle_bls12_377::curve::{CurveCfg, G1Projective, ScalarCfg}; +use icicle_core::{curve::Curve, msm, msm::MSMConfig, traits::GenerateRandom}; +use icicle_runtime::{device::Device, memory::HostSlice}; + +fn main() { + // Load backend and set device ... + + // Randomize inputs + let size = 1024; + let points = CurveCfg::generate_random_affine_points(size); + let scalars = ScalarCfg::generate_random(size); + + let mut msm_results = vec![G1Projective::zero(); 1]; + msm::msm( + HostSlice::from_slice(&scalars), + HostSlice::from_slice(&points), + &MSMConfig::default(), + HostSlice::from_mut_slice(&mut msm_results[..]), + ) + .unwrap(); + println!("MSM result = {:?}", msm_results); +} + +``` + +## Batched msm + +For batch msm, simply allocate the results array with size corresponding to batch size and set the `are_points_shared_in_batch` flag in config struct. + +## Precomputationg + +Precomputes bases for the multi-scalar multiplication (MSM) by extending each base point with its multiples, facilitating more efficient MSM calculations. + +```rust +/// Returns `Ok(())` if no errors occurred or a `eIcicleError` otherwise. +pub fn precompute_bases>( + points: &(impl HostOrDeviceSlice> + ?Sized), + config: &MSMConfig, + output_bases: &mut DeviceSlice>, +) -> Result<(), eIcicleError>; +``` + +### Parameters + +- **`points`**: The original set of affine points (\(P_1, P_2, ..., P_n\)) to be used in the MSM. For batch MSM operations, this should include all unique points concatenated together. +- **`msm_size`**: The size of a single msm in order to determine optimal parameters. +- **`cfg`**: The MSM configuration parameters. +- **`output_bases`**: The output buffer for the extended bases. Its size must be `points.len() * precompute_factor`. This buffer should be allocated on the device for GPU computations. + +#### Returns + +`Ok(())` if the operation is successful, or an `eIcicleError` error otherwise. + +## Parameters for optimal performance + +Please refer to the [primitive description](../primitives/msm#choosing-optimal-parameters) diff --git a/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/multi-gpu.md b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/multi-gpu.md new file mode 100644 index 000000000..527e780ee --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/multi-gpu.md @@ -0,0 +1,204 @@ +# Multi GPU APIs + +TODO update for V3 + +To learn more about the theory of Multi GPU programming refer to [this part](../multi-device.md) of documentation. + +Here we will cover the core multi GPU apis and a [example](#a-multi-gpu-example) + + +## A Multi GPU example + +In this example we will display how you can + +1. Fetch the number of devices installed on a machine +2. For every GPU launch a thread and set an active device per thread. +3. Execute a MSM on each GPU + + + +```rust + +... + +let device_count = get_device_count().unwrap(); + +(0..device_count) + .into_par_iter() + .for_each(move |device_id| { + set_device(device_id).unwrap(); + + // you can allocate points and scalars_d here + + let mut cfg = MSMConfig::default_for_device(device_id); + cfg.ctx.stream = &stream; + cfg.is_async = true; + cfg.are_scalars_montgomery_form = true; + msm(&scalars_d, &HostOrDeviceSlice::on_host(points), &cfg, &mut msm_results).unwrap(); + + // collect and process results + }) + +... +``` + + +We use `get_device_count` to fetch the number of connected devices, device IDs will be `0, 1, 2, ..., device_count - 1` + +[`into_par_iter`](https://docs.rs/rayon/latest/rayon/iter/trait.IntoParallelIterator.html#tymethod.into_par_iter) is a parallel iterator, you should expect it to launch a thread for every iteration. + +We then call `set_device(device_id).unwrap();` it should set the context of that thread to the selected `device_id`. + +Any data you now allocate from the context of this thread will be linked to the `device_id`. We create our `MSMConfig` with the selected device ID `let mut cfg = MSMConfig::default_for_device(device_id);`, behind the scene this will create for us a `DeviceContext` configured for that specific GPU. + +We finally call our `msm` method. + + +## Device management API + +To streamline device management we offer as part of `icicle-cuda-runtime` package methods for dealing with devices. + +#### [`set_device`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L6) + +Sets the current CUDA device by its ID, when calling `set_device` it will set the current thread to a CUDA device. + +**Parameters:** + +- **`device_id: usize`**: The ID of the device to set as the current device. Device IDs start from 0. + +**Returns:** + +- **`CudaResult<()>`**: An empty result indicating success if the device is set successfully. In case of failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if the specified device ID is invalid or if a CUDA-related error occurs during the operation. + +**Example:** + +```rust +let device_id = 0; // Device ID to set +match set_device(device_id) { + Ok(()) => println!("Device set successfully."), + Err(e) => eprintln!("Failed to set device: {:?}", e), +} +``` + +#### [`get_device_count`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L10) + +Retrieves the number of CUDA devices available on the machine. + +**Returns:** + +- **`CudaResult`**: The number of available CUDA devices. On success, contains the count of CUDA devices. On failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if a CUDA-related error occurs during the retrieval of the device count. + +**Example:** + +```rust +match get_device_count() { + Ok(count) => println!("Number of devices available: {}", count), + Err(e) => eprintln!("Failed to get device count: {:?}", e), +} +``` + +#### [`get_device`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device.rs#L15) + +Retrieves the ID of the current CUDA device. + +**Returns:** + +- **`CudaResult`**: The ID of the current CUDA device. On success, contains the device ID. On failure, returns a `CudaError`. + +**Errors:** + +- Returns a `CudaError` if a CUDA-related error occurs during the retrieval of the current device ID. + +**Example:** + +```rust +match get_device() { + Ok(device_id) => println!("Current device ID: {}", device_id), + Err(e) => eprintln!("Failed to get current device: {:?}", e), +} +``` + +## Device context API + +The `DeviceContext` is embedded into `NTTConfig`, `MSMConfig` and `PoseidonConfig`, meaning you can simply pass a `device_id` to your existing config and the same computation will be triggered on a different device. + +#### [`DeviceContext`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L11) + +Represents the configuration a CUDA device, encapsulating the device's stream, ID, and memory pool. The default device is always `0`. + +```rust +pub struct DeviceContext<'a> { + pub stream: &'a CudaStream, + pub device_id: usize, + pub mempool: CudaMemPool, +} +``` + +##### Fields + +- **`stream: &'a CudaStream`** + + A reference to a `CudaStream`. This stream is used for executing CUDA operations. By default, it points to a null stream CUDA's default execution stream. + +- **`device_id: usize`** + + The index of the GPU currently in use. The default value is `0`, indicating the first GPU in the system. + + In some cases assuming `CUDA_VISIBLE_DEVICES` was configured, for example as `CUDA_VISIBLE_DEVICES=2,3,7` in the system with 8 GPUs - the `device_id=0` will correspond to GPU with id 2. So the mapping may not always be a direct reflection of the number of GPUs installed on a system. + +- **`mempool: CudaMemPool`** + + Represents the memory pool used for CUDA memory allocations. The default is set to a null pointer, which signifies the use of the default CUDA memory pool. + +##### Implementation Notes + +- The `DeviceContext` structure is cloneable and can be debugged, facilitating easier logging and duplication of contexts when needed. + + +#### [`DeviceContext::default_for_device(device_id: usize) -> DeviceContext<'static>`](https://github.com/ingonyama-zk/icicle/blob/e6035698b5e54632f2c44e600391352ccc11cad4/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L30) + +Provides a default `DeviceContext` with system-wide defaults, ideal for straightforward setups. + +#### Returns + +A `DeviceContext` instance configured with: +- The default stream (`null_mut()`). +- The default device ID (`0`). +- The default memory pool (`null_mut()`). + +#### Parameters + +- **`device_id: usize`**: The ID of the device for which to create the context. + +#### Returns + +A `DeviceContext` instance with the provided `device_id` and default settings for the stream and memory pool. + + +#### [`check_device(device_id: i32)`](https://github.com/vhnatyk/icicle/blob/eef6876b037a6b0797464e7cdcf9c1ecfcf41808/wrappers/rust/icicle-cuda-runtime/src/device_context.rs#L42) + +Validates that the specified `device_id` matches the ID of the currently active device, ensuring operations are targeted correctly. + +#### Parameters + +- **`device_id: i32`**: The device ID to verify against the currently active device. + +#### Behavior + +- **`Panics`** if the `device_id` does not match the active device's ID, preventing cross-device operation errors. + +#### Example + +```rust +let device_id: i32 = 0; // Example device ID +check_device(device_id); +// Ensures that the current context is correctly set for the specified device ID. +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/ntt.md b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/ntt.md new file mode 100644 index 000000000..06758972e --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/ntt.md @@ -0,0 +1,106 @@ +# NTT + +## NTT API overview + +```rust +pub fn ntt( + input: &(impl HostOrDeviceSlice + ?Sized), + dir: NTTDir, + cfg: &NTTConfig, + output: &mut (impl HostOrDeviceSlice + ?Sized), +) -> Result<(), eIcicleError>; + +pub fn ntt_inplace( + inout: &mut (impl HostOrDeviceSlice + ?Sized), + dir: NTTDir, + cfg: &NTTConfig, +) -> Result<(), eIcicleError> +``` + +- **`input`** - buffer to read the inputs of the NTT from. +- **`dir`** - whether to compute forward or inverse NTT. +- **`cfg`** - config used to specify extra arguments of the NTT. +- **`output`** - buffer to write the NTT outputs into. Must be of the same size as input. + +The `input` and `output` buffers can be on device or on host. Being on host means that they will be transferred to device during runtime. + +### NTT Config + +```rust +pub struct NTTConfig { + pub stream_handle: IcicleStreamHandle, + pub coset_gen: S, + pub batch_size: i32, + pub columns_batch: bool, + pub ordering: Ordering, + pub are_inputs_on_device: bool, + pub are_outputs_on_device: bool, + pub is_async: bool, + pub ext: ConfigExtension, +} +``` + +The `NTTConfig` struct is a configuration object used to specify parameters for an NTT instance. + +#### Fields + +- **`stream_handle: IcicleStreamHandle`**: Specifies the stream (queue) to use for async execution + +- **`coset_gen: S`**: Defines the coset generator used for coset (i)NTTs. By default, this is set to `S::one()`, indicating that no coset is being used. + +- **`batch_size: i32`**: Determines the number of NTTs to compute in a single batch. The default value is 1, meaning that operations are performed on individual inputs without batching. Batch processing can significantly improve performance by leveraging parallelism in GPU computations. + +- **`columns_batch`**: If true the function will compute the NTTs over the columns of the input matrix and not over the rows. Defaults to `false`. + +- **`ordering: Ordering`**: Controls the ordering of inputs and outputs for the NTT operation. This field can be used to specify decimation strategies (in time or in frequency) and the type of butterfly algorithm (Cooley-Tukey or Gentleman-Sande). The ordering is crucial for compatibility with various algorithmic approaches and can impact the efficiency of the NTT. + +- **`are_inputs_on_device: bool`**: Indicates whether the input data has been preloaded on the device memory. If `false` inputs will be copied from host to device. + +- **`are_outputs_on_device: bool`**: Indicates whether the output data is preloaded in device memory. If `false` outputs will be copied from host to device. If the inputs and outputs are the same pointer NTT will be computed in place. + +- **`is_async: bool`**: Specifies whether the NTT operation should be performed asynchronously. When set to `true`, the NTT function will not block the CPU, allowing other operations to proceed concurrently. Asynchronous execution requires careful synchronization to ensure data integrity and correctness. +- **`ext: ConfigExtension`**: extended configuration for backend. + +#### Example + +```rust +// Setting Bn254 points and scalars +println!("Generating random inputs on host for bn254..."); +let scalars = Bn254ScalarCfg::generate_random(size); +let mut ntt_results = DeviceVec::::device_malloc(size).unwrap(); + +// constructing NTT domain +initialize_domain( + ntt::get_root_of_unity::( + size.try_into() + .unwrap(), + ), + &ntt::NTTInitDomainConfig::default(), +) +.unwrap(); + +// Using default config +let cfg = ntt::NTTConfig::::default(); + +// Computing NTT +ntt::ntt( + HostSlice::from_slice(&scalars), + ntt::NTTDir::kForward, + &cfg, + &mut ntt_results[..], +) +.unwrap(); +``` + +### NTT Domain + +Before performing NTT operations, it is mandatory to construct the domain as [explained here](../primitives/ntt.md#ntt-domain). +In rust, we have the following functions to construct, destruct the domain and retrieve a root of unity from it: + +```rust +pub trait NTTDomain { + pub fn initialize_domain(primitive_root: F, config: &NTTInitDomainConfig) -> Result<(), eIcicleError>; + pub fn release_domain() -> Result<(), eIcicleError>; + pub fn get_root_of_unity(max_size: u64) -> F; +} +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/polynomials.md b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/polynomials.md new file mode 100644 index 000000000..edb99c917 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/polynomials.md @@ -0,0 +1,284 @@ +# Rust FFI Bindings for Univariate Polynomial + +:::note +Please refer to the Polynomials overview page for a deep overview. This section is a brief description of the Rust FFI bindings. +::: + +This documentation is designed to provide developers with a clear understanding of how to utilize the Rust bindings for polynomial operations efficiently and effectively, leveraging the robust capabilities of both Rust and C++ in their applications. + +## Introduction + +The Rust FFI bindings for the Univariate Polynomial serve as a "shallow wrapper" around the underlying C++ implementation. These bindings provide a straightforward Rust interface that directly calls functions from a C++ library, effectively bridging Rust and C++ operations. The Rust layer handles simple interface translations without delving into complex logic or data structures, which are managed on the C++ side. This design ensures efficient data handling, memory management, and execution of polynomial operations directly via C++. +Currently, these bindings are tailored specifically for polynomials where the coefficients, domain, and images are represented as scalar fields. + +## Initialization Requirements + +Before utilizing any functions from the polynomial API, it is mandatory to initialize the appropriate polynomial backend (e.g., CUDA). Additionally, the NTT (Number Theoretic Transform) domain must also be initialized, as the CUDA backend relies on this for certain operations. Failing to properly initialize these components can result in errors. + +:::note +**Field-Specific Initialization Requirement** + +The ICICLE library is structured such that each field or curve has its dedicated library implementation. As a result, initialization must be performed individually for each field or curve to ensure the correct setup and functionality of the library. +::: + +## Core Trait: `UnivariatePolynomial` + +The `UnivariatePolynomial` trait encapsulates the essential functionalities required for managing univariate polynomials in the Rust ecosystem. This trait standardizes the operations that can be performed on polynomials, regardless of the underlying implementation details. It allows for a unified approach to polynomial manipulation, providing a suite of methods that are fundamental to polynomial arithmetic. + +### Trait Definition + +```rust +pub trait UnivariatePolynomial +where + Self::Field: FieldImpl, + Self::FieldConfig: FieldConfig, +{ + type Field: FieldImpl; + type FieldConfig: FieldConfig; + + // Methods to create polynomials from coefficients or roots-of-unity evaluations. + fn from_coeffs + ?Sized>(coeffs: &S, size: usize) -> Self; + fn from_rou_evals + ?Sized>(evals: &S, size: usize) -> Self; + + // Method to divide this polynomial by another, returning quotient and remainder. + fn divide(&self, denominator: &Self) -> (Self, Self) where Self: Sized; + + // Method to divide this polynomial by the vanishing polynomial 'X^N-1'. + fn div_by_vanishing(&self, degree: u64) -> Self; + + // Methods to add or subtract a monomial in-place. + fn add_monomial_inplace(&mut self, monomial_coeff: &Self::Field, monomial: u64); + fn sub_monomial_inplace(&mut self, monomial_coeff: &Self::Field, monomial: u64); + + // Method to slice the polynomial, creating a sub-polynomial. + fn slice(&self, offset: u64, stride: u64, size: u64) -> Self; + + // Methods to return new polynomials containing only the even or odd terms. + fn even(&self) -> Self; + fn odd(&self) -> Self; + + // Method to evaluate the polynomial at a given domain point. + fn eval(&self, x: &Self::Field) -> Self::Field; + + // Method to evaluate the polynomial over a domain and store the results. + fn eval_on_domain + ?Sized, E: HostOrDeviceSlice + ?Sized>( + &self, + domain: &D, + evals: &mut E, + ); + + // Method to evaluate the polynomial over the roots-of-unity domain for power-of-two sized domain + fn eval_on_rou_domain + ?Sized>(&self, domain_log_size: u64, evals: &mut E); + + // Method to retrieve a coefficient at a specific index. + fn get_coeff(&self, idx: u64) -> Self::Field; + + // Method to copy coefficients into a provided slice. + fn copy_coeffs + ?Sized>(&self, start_idx: u64, coeffs: &mut S); + + // Method to get the degree of the polynomial. + fn degree(&self) -> i64; +} +``` + +## `DensePolynomial` Struct + +The DensePolynomial struct represents a dense univariate polynomial in Rust, leveraging a handle to manage its underlying memory within the CUDA device context. This struct acts as a high-level abstraction over complex C++ memory management practices, facilitating the integration of high-performance polynomial operations through Rust's Foreign Function Interface (FFI) bindings. + +```rust +pub struct DensePolynomial { + handle: PolynomialHandle, +} +``` + +### Traits implementation and methods + +#### `Drop` + +Ensures proper resource management by releasing the CUDA memory when a DensePolynomial instance goes out of scope. This prevents memory leaks and ensures that resources are cleaned up correctly, adhering to Rust's RAII (Resource Acquisition Is Initialization) principles. + +#### `Clone` + +Provides a way to create a new instance of a DensePolynomial with its own unique handle, thus duplicating the polynomial data in the CUDA context. Cloning is essential since the DensePolynomial manages external resources, which cannot be safely shared across instances without explicit duplication. + +#### Operator Overloading: `Add`, `Sub`, `Mul`, `Rem`, `Div` + +These traits are implemented for references to DensePolynomial (i.e., &DensePolynomial), enabling natural mathematical operations such as addition (+), subtraction (-), multiplication (*), division (/), and remainder (%). This syntactic convenience allows users to compose complex polynomial expressions in a way that is both readable and expressive. + +#### Key Methods + +In addition to the traits, the following methods are implemented: + +```rust +impl DensePolynomial { + // Returns a mutable slice of the polynomial coefficients on the device + pub fn coeffs_mut_slice(&mut self) -> &mut DeviceSlice {...} +} +``` + +## Flexible Memory Handling With `HostOrDeviceSlice` + +The DensePolynomial API is designed to accommodate a wide range of computational environments by supporting both host and device memory through the `HostOrDeviceSlice` trait. This approach ensures that polynomial operations can be seamlessly executed regardless of where the data resides, making the API highly adaptable and efficient for various hardware configurations. + +### Overview of `HostOrDeviceSlice` + +The HostOrDeviceSlice is a Rust trait that abstracts over slices of memory that can either be on the host (CPU) or the device (GPU), as managed by CUDA. This abstraction is crucial for high-performance computing scenarios where data might need to be moved between different memory spaces depending on the operations being performed and the specific hardware capabilities available. + +### Usage in API Functions + +Functions within the DensePolynomial API that deal with polynomial coefficients or evaluations use the HostOrDeviceSlice trait to accept inputs. This design allows the functions to be agnostic of the actual memory location of the data, whether it's in standard system RAM accessible by the CPU or in GPU memory accessible by CUDA cores. + +```rust +// Assume `coeffs` could either be in host memory or CUDA device memory +let coeffs: DeviceSlice = DeviceVec::::device_malloc(coeffs_len).unwrap(); +let p_from_coeffs = PolynomialBabyBear::from_coeffs(&coeffs, coeffs.len()); + +// Similarly for evaluations from roots of unity +let evals: HostSlice = HostSlice::from_slice(&host_memory_evals); +let p_from_evals = PolynomialBabyBear::from_rou_evals(&evals, evals.len()); + +// Same applies for any API that accepts HostOrDeviceSlice +``` + +## Usage + +This section outlines practical examples demonstrating how to utilize the `DensePolynomial` Rust API. The API is flexible, supporting multiple scalar fields. Below are examples showing how to use polynomials defined over different fields and perform a variety of operations. + +### Initialization and Basic Operations + +First, choose the appropriate field implementation for your polynomial operations, initializing the CUDA backend if necessary + +```rust +use icicle_babybear::polynomials::DensePolynomial as PolynomialBabyBear; + +let f = PolynomialBabyBear::from_coeffs(...); + +// now use f by calling the implemented traits + +// For operations over another field, such as BN254 +use icicle_bn254::polynomials::DensePolynomial as PolynomialBn254; +// Use PolynomialBn254 similarly +``` + +### Creation + +Polynomials can be created from coefficients or evaluations: + +```rust +let coeffs = ...; +let p_from_coeffs = PolynomialBabyBear::from_coeffs(HostSlice::from_slice(&coeffs), size); + +let evals = ...; +let p_from_evals = PolynomialBabyBear::from_rou_evals(HostSlice::from_slice(&evals), size); + +``` + +### Arithmetic Operations + +Utilize overloaded operators for intuitive mathematical expressions: + +```rust +let add = &f + &g; // Addition +let sub = &f - &g; // Subtraction +let mul = &f * &g; // Multiplication +let mul_scalar = &f * &scalar; // Scalar multiplication +``` + +### Division and Remainder + +Compute quotient and remainder or perform division by a vanishing polynomial: + +```rust +let (q, r) = f.divide(&g); // Compute both quotient and remainder +let q = &f / &g; // Quotient +let r = &f % &g; // Remainder + +let h = f.div_by_vanishing(N); // Division by V(x) = X^N - 1 + +``` + +### Monomial Operations + +Add or subtract monomials in-place for efficient polynomial manipulation: + +```rust +f.add_monomial_inplace(&three, 1 /*monmoial*/); // Adds 3*x to f +f.sub_monomial_inplace(&one, 0 /*monmoial*/); // Subtracts 1 from f +``` + +### Slicing + +Extract specific components: + +```rust +let even = f.even(); // Polynomial of even-indexed terms +let odd = f.odd(); // Polynomial of odd-indexed terms +let arbitrary_slice = f.slice(offset, stride, size); +``` + +### Evaluate + +Evaluate the polynoomial: + +```rust +let x = rand(); // Random field element +let f_x = f.eval(&x); // Evaluate f at x + +// Evaluate on a predefined domain +let domain = [one, two, three]; +let mut host_evals = vec![ScalarField::zero(); domain.len()]; +f.eval_on_domain(HostSlice::from_slice(&domain), HostSlice::from_mut_slice(&mut host_evals)); + +// Evaluate on roots-of-unity-domain +let domain_log_size = 4; +let mut device_evals = DeviceVec::::device_malloc(1 << domain_log_size).unwrap(); +f.eval_on_rou_domain(domain_log_size, &mut device_evals[..]); +``` + +### Read coefficients + +Read or copy polynomial coefficients for further processing: + +```rust +let x_squared_coeff = f.get_coeff(2); // Coefficient of x^2 + +// Copy coefficients to a device-specific memory space +let mut device_mem = DeviceVec::::device_malloc(coeffs.len()).unwrap(); +f.copy_coeffs(0, &mut device_mem[..]); +``` + +### Polynomial Degree + +Determine the highest power of the variable with a non-zero coefficient: + +```rust +let deg = f.degree(); // Degree of the polynomial +``` + +### Memory Management: Views (rust slices) + +Rust enforces correct usage of views at compile time, eliminating the need for runtime checks: + +```rust +let mut f = Poly::from_coeffs(HostSlice::from_slice(&coeffs), size); + +// Obtain a mutable slice of coefficients as a DeviceSlice +let coeffs_slice_dev = f.coeffs_mut_slice(); + +// Operations on f are restricted here due to mutable borrow of coeffs_slice_dev + +// Compute evaluations or perform other operations directly using the slice +// example: evaluate f on a coset of roots-of-unity. Computing from GPU to HOST/GPU +let mut config: NTTConfig<'_, F> = NTTConfig::default(); +config.coset_gen = /*some coset gen*/; +let mut coset_evals = vec![F::zero(); coeffs_slice_dev.len()]; +ntt( + coeffs_slice_dev, + NTTDir::kForward, + &config, + HostSlice::from_mut_slice(&mut coset_evals), +) +.unwrap(); + +// now can f can be borrowed once again +``` diff --git a/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/vec-ops.md b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/vec-ops.md new file mode 100644 index 000000000..c42caafb5 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/icicle/rust-bindings/vec-ops.md @@ -0,0 +1,109 @@ +# Vector Operations API + +Our vector operations API includes fundamental methods for addition, subtraction, and multiplication of vectors, with support for both host and device memory, as well as batched operations. + +## Vector Operations Configuration + +The `VecOpsConfig` struct encapsulates the settings for vector operations, including device context, operation modes, and batching parameters. + +### `VecOpsConfig` + +Defines configuration parameters for vector operations. + +```rust +pub struct VecOpsConfig { + pub stream_handle: IcicleStreamHandle, + pub is_a_on_device: bool, + pub is_b_on_device: bool, + pub is_result_on_device: bool, + pub is_async: bool, + pub batch_size: usize, + pub columns_batch: bool, + pub ext: ConfigExtension, +} +``` + +#### Fields + +- **`stream_handle: IcicleStreamHandle`**: Specifies the stream (queue) to use for async execution +- **`is_a_on_device: bool`**: Indicates whether the input data a has been preloaded on the device memory. If `false` inputs will be copied from host to device. +- **`is_b_on_device: bool`**: Indicates whether the input b data has been preloaded on the device memory. If `false` inputs will be copied from host to device. +- **`is_result_on_device: bool`**: Indicates whether the output data is preloaded in device memory. If `false` outputs will be copied from host to device. +- **`is_async: bool`**: Specifies whether the NTT operation should be performed asynchronously. +- **`batch_size: usize`**: Number of vector operations to process in a single batch. Each operation will be performed independently on each batch element. +- **`columns_batch: bool`**: true if the batched vectors are stored as columns in a 2D array (i.e., the vectors are strided in memory as columns of a matrix). If false, the batched vectors are stored contiguously in memory (e.g., as rows or in a flat array). + +- **`ext: ConfigExtension`**: extended configuration for backend. + +### Default Configuration + +`VecOpsConfig` can be initialized with default settings tailored for a specific device: + +```rust +let cfg = VecOpsConfig::default(); +``` + +## Vector Operations + +Vector operations are implemented through the `VecOps` trait, providing methods for addition, subtraction, and multiplication of vectors. These methods support both single and batched operations based on the batch_size and columns_batch configurations. + +### Methods + +All operations are element-wise operations, and the results placed into the `result` param. These operations are not in place, except for accumulate. + +- **`add`**: Computes the element-wise sum of two vectors. +- **`accumulate`**: Sum input b to a inplace. +- **`sub`**: Computes the element-wise difference between two vectors. +- **`mul`**: Performs element-wise multiplication of two vectors. +- **`transpose`**: Performs matrix transpose. +- **`bit_reverse/bit_reverse_inplace`**: Reverse order of elements based on bit-reverse. + + + +```rust +pub fn add_scalars( + a: &(impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + result: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn accumulate_scalars( + a: &mut (impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn sub_scalars( + a: &(impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + result: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn mul_scalars( + a: &(impl HostOrDeviceSlice + ?Sized), + b: &(impl HostOrDeviceSlice + ?Sized), + result: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn transpose_matrix( + input: &(impl HostOrDeviceSlice + ?Sized), + nof_rows: u32, + nof_cols: u32, + output: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; + +pub fn bit_reverse( + input: &(impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, + output: &mut (impl HostOrDeviceSlice + ?Sized), +) -> Result<(), eIcicleError>; + +pub fn bit_reverse_inplace( + input: &mut (impl HostOrDeviceSlice + ?Sized), + cfg: &VecOpsConfig, +) -> Result<(), eIcicleError>; +``` \ No newline at end of file diff --git a/docs/versioned_docs/version-3.1.0/introduction.md b/docs/versioned_docs/version-3.1.0/introduction.md new file mode 100644 index 000000000..b173cf5f9 --- /dev/null +++ b/docs/versioned_docs/version-3.1.0/introduction.md @@ -0,0 +1,42 @@ +--- +slug: / +displayed_sidebar: GettingStartedSidebar +title: '' +--- + +# Welcome to Ingonyama's Developer Documentation + +Ingonyama is a next-generation semiconductor company focusing on Zero-Knowledge Proof hardware acceleration. We build accelerators for advanced cryptography, unlocking real-time applications. Our focus is on democratizing access to compute-intensive cryptography and making it accessible for developers to build upon. + +Our flagship product is **ICICLE** + +#### **ICICLE v3** +[ICICLE v3](https://github.com/ingonyama-zk/icicle) is a versatile cryptography library designed to support multiple compute backends, including CUDA, CPU, and potentially others like Metal, WebGPU, Vulkan, and ZPU. Originally focused on GPU acceleration, ICICLE has evolved to offer backend-agnostic cryptographic acceleration, allowing you to build ZK provers or other cryptographic applications with ease, leveraging the best available hardware for your needs. + +- **Multiple Backend Support:** Develop on CPU and deploy on various backends including CUDA and potentially Metal, WebGPU, Vulkan, ZPU, or even remote machines. +- **Cross-Language Compatibility:** Use ICICLE across multiple programming languages such as C++, Rust, Go, and possibly Python. +- **Optimized for ZKPs:** Accelerate cryptographic operations like elliptic curve operations, MSM, NTT, Poseidon hash, and more. + +**Learn more about ICICLE and its multi-backend support [here][ICICLE-OVERVIEW].** + +--- + +## Our Approach to Hardware Acceleration + +We believe that GPUs are as critical for ZK as they are for AI. + +- **Parallelism:** Approximately 97% of ZK protocol runtime is naturally parallel, making GPUs an ideal match. +- **Developer-Friendly:** GPUs offer simplicity in scaling and usage compared to other hardware platforms. +- **Cost-Effective:** GPUs provide a competitive balance of power, performance, and cost, often being 3x cheaper than FPGAs. + +For a more in-depth understanding on this topic we suggest you read [our article on the subject](https://www.ingonyama.com/blog/revisiting-paradigm-hardware-acceleration-for-zero-knowledge-proofs). + + +## Get in Touch + +If you have any questions, ideas, or are thinking of building something in this space, join the discussion on [Discord]. You can explore our code on [github](https://github.com/ingonyama-zk) or read some of [our research papers](https://github.com/ingonyama-zk/papers). + +Follow us on [Twitter](https://x.com/Ingo_zk) and [YouTube](https://www.youtube.com/@ingo_ZK) and join us IRL at our [next event](https://www.ingonyama.com/events) + +[ICICLE-OVERVIEW]: ./icicle/overview.md +[Discord]: https://discord.gg/6vYrE7waPj diff --git a/docs/versioned_sidebars/version-1.10.1-sidebars.json b/docs/versioned_sidebars/version-1.10.1-sidebars.json new file mode 100644 index 000000000..32a822a7d --- /dev/null +++ b/docs/versioned_sidebars/version-1.10.1-sidebars.json @@ -0,0 +1,197 @@ +{ + "GettingStartedSidebar": [ + { + "type": "doc", + "label": "Introduction", + "id": "introduction" + }, + { + "type": "category", + "label": "ICICLE", + "link": { + "type": "doc", + "id": "icicle/overview" + }, + "collapsed": false, + "items": [ + { + "type": "doc", + "label": "Getting started", + "id": "icicle/introduction" + }, + { 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"NTT / ECNTT", + "id": "icicle/primitives/ntt" + }, + { + "type": "doc", + "label": "Vector operations", + "id": "icicle/primitives/vec_ops" + }, + { + "type": "doc", + "label": "Polynomials", + "id": "icicle/polynomials/overview" + }, + { + "type": "doc", + "label": "Hash", + "id": "icicle/primitives/hash" + }, + { + "type": "doc", + "label": "Merkle-Tree", + "id": "icicle/primitives/merkle" + }, + { + "type": "category", + "label": "Golang bindings", + "link": { + "type": "doc", + "id": "icicle/golang-bindings" + }, + "collapsed": true, + "items": [ + { + "type": "category", + "label": "MSM", + "link": { + "type": "doc", + "id": "icicle/golang-bindings/msm" + }, + "collapsed": true, + "items": [ + { + "type": "doc", + "label": "MSM pre computation", + "id": "icicle/golang-bindings/msm-pre-computation" + } + ] + }, + { + "type": "doc", + "label": "NTT", + "id": "icicle/golang-bindings/ntt" + }, + { + "type": "doc", + "label": "EC-NTT", + "id": "icicle/golang-bindings/ecntt" + }, + { + "type": "doc", + "label": "Vector operations", + "id": "icicle/golang-bindings/vec-ops" + }, + { + "type": "doc", + "label": "Multi GPU Support", + "id": "icicle/golang-bindings/multi-gpu" + }, + { + "type": "doc", + "label": "Hash", + "id": "icicle/golang-bindings/hash" + }, + { + "type": "doc", + "label": "Merkle-Tree", + "id": "icicle/golang-bindings/merkle" + } + ] + }, + { + "type": "category", + "label": "Rust bindings", + "link": { + "type": "doc", + "id": "icicle/rust-bindings" + }, + "collapsed": true, + "items": [ + { + "type": "doc", + "label": "MSM", + "id": "icicle/rust-bindings/msm" + }, + { + "type": "doc", + "label": "NTT", + "id": "icicle/rust-bindings/ntt" + }, + { + "type": "doc", + "label": "ECNTT", + "id": "icicle/rust-bindings/ecntt" + }, + { + "type": "doc", + "label": "Vector operations", + "id": "icicle/rust-bindings/vec-ops" + }, + { + "type": "doc", + "label": "Polynomials", + "id": "icicle/rust-bindings/polynomials" + }, + { + "type": "doc", + "label": "Hash", + "id": "icicle/rust-bindings/hash" + }, + { + "type": "doc", + "label": "Merkle-Tree", + "id": "icicle/rust-bindings/merkle" + } + ] + } + ] + }, + { + "type": "doc", + "label": "Migrate from ICICLE v2", + "id": "icicle/migrate_from_v2" + }, + { + "type": "doc", + "label": "Google Colab Instructions", + "id": "icicle/colab-instructions" + }, + { + "type": "doc", + "label": "ICICLE Provers", + "id": "icicle/integrations" + } + ] + }, + { + "type": "doc", + "label": "Ingonyama Grant program", + "id": "grants" + }, + { + "type": "doc", + "label": "Contributor guide", + "id": "contributor-guide" + }, + { + "type": "category", + "label": "Additional Resources", + "collapsed": false, + "collapsible": false, + "items": [ + { + "type": "link", + "label": "YouTube", + "href": "https://www.youtube.com/@ingo_ZK" + }, + { + "type": "link", + "label": "Ingonyama Blog", + "href": "https://www.ingonyama.com/blog" + }, + { + "type": "link", + "label": "Ingopedia", + "href": "https://www.ingonyama.com/ingopedia" + }, + { + "href": "https://github.com/ingonyama-zk", + "type": "link", + "label": "GitHub" + } + ] + } + ] +} diff --git a/docs/versions.json b/docs/versions.json new file mode 100644 index 000000000..1017c76a3 --- /dev/null +++ b/docs/versions.json @@ -0,0 +1,6 @@ +[ + "3.1.0", + "3.0.0", + "2.8.0", + "1.10.1" +] diff --git a/scripts/release/build_all.sh b/scripts/release/build_all.sh index 3930d3a6a..965f01c80 100755 --- a/scripts/release/build_all.sh +++ b/scripts/release/build_all.sh @@ -4,6 +4,20 @@ set -e # Use provided release_output directory or default to "release_output" output_dir="${1:-./release_output}" +version="$2" + +if [[ -z $version ]]; then + echo "Usage: You must supply a version for release tar files" + exit 1 +fi + +first_char=${version:0:1} +if [[ "${first_char,,}" == "v" ]]; then + version="${version:1}" +fi + + +version="${version//./_}" # Check if both directories exist in the current working directory if [[ ! -d "./icicle" || ! -d "./scripts" ]]; then @@ -32,25 +46,25 @@ docker run --rm --gpus all \ -v ./icicle:/icicle \ -v "$output_dir:/output" \ -v ./scripts:/scripts \ - icicle-release-ubuntu22-cuda122 bash /scripts/release/build_release_and_tar.sh icicle_3_1_0 ubuntu22 cuda122 & + icicle-release-ubuntu22-cuda122 bash /scripts/release/build_release_and_tar.sh icicle_$version ubuntu22 cuda122 & # ubuntu 20 docker run --rm --gpus all \ -v ./icicle:/icicle \ -v "$output_dir:/output" \ -v ./scripts:/scripts \ - icicle-release-ubuntu20-cuda122 bash /scripts/release/build_release_and_tar.sh icicle_3_1_0 ubuntu20 cuda122 & + icicle-release-ubuntu20-cuda122 bash /scripts/release/build_release_and_tar.sh icicle_$version ubuntu20 cuda122 & # ubi 8 (rhel compatible) docker run --rm --gpus all \ -v ./icicle:/icicle \ -v "$output_dir:/output" \ -v ./scripts:/scripts \ - icicle-release-ubi8-cuda122 bash /scripts/release/build_release_and_tar.sh icicle_3_1_0 ubi8 cuda122 & + icicle-release-ubi8-cuda122 bash /scripts/release/build_release_and_tar.sh icicle_$version ubi8 cuda122 & # ubi 9 (rhel compatible) docker run --rm --gpus all \ -v ./icicle:/icicle \ -v "$output_dir:/output" \ -v ./scripts:/scripts \ - icicle-release-ubi9-cuda122 bash /scripts/release/build_release_and_tar.sh icicle_3_1_0 ubi9 cuda122 & + icicle-release-ubi9-cuda122 bash /scripts/release/build_release_and_tar.sh icicle_$version ubi9 cuda122 &