oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. oneDNN project is part of the UXL Foundation and is an implementation of the oneAPI specification for oneDNN component.
The library is optimized for Intel(R) Architecture Processors, Intel Graphics, and Arm(R) 64-bit Architecture (AArch64)-based processors. oneDNN has experimental support for the following architectures: NVIDIA* GPU, AMD* GPU, OpenPOWER* Power ISA (PPC64), IBMz* (s390x), and RISC-V.
oneDNN is intended for deep learning applications and framework developers interested in improving application performance on CPUs and GPUs. Deep learning practitioners should use one of the applications enabled with oneDNN.
- Documentation
- Installation
- System Requirements
- Applications Enabled with oneDNN
- Governance
- Support
- Contributing
- License
- Security
- Trademark Information
- Developer Guide explains the programming model, supported functionality, and implementation details, and includes annotated examples.
- API Reference provides a comprehensive reference of the library API.
Binary distribution of this software is available in:
The packages do not include library dependencies and these need to be resolved in the application at build time. See the System Requirements section below and the Build Options section in the Developer Guide for more details on CPU and GPU runtimes.
If the configuration you need is not available, you can build the library from source.
oneDNN supports platforms based on the following architectures:
- Intel 64 or AMD64,
- Arm 64-bit Architecture (AArch64).
- OpenPOWER / IBM Power ISA.
- IBMz z/Architecture (s390x).
- RISC-V 64-bit (RV64).
WARNING
Power ISA (PPC64), IBMz (s390x), and RISC-V (RV64) support is experimental with limited testing validation.
The library is optimized for the following CPUs:
- Intel 64/AMD64 architecture
- Intel Atom(R) processor (at least Intel SSE4.1 support is required)
- Intel Core(TM) processor (at least Intel SSE4.1 support is required)
- Intel Xeon(R) processor E3, E5, and E7 family (formerly Sandy Bridge, Ivy Bridge, Haswell, and Broadwell)
- Intel Xeon Scalable processor (formerly Skylake, Cascade Lake, Cooper Lake, Ice Lake, Sapphire Rapids, and Emerald Rapids)
- Intel Xeon CPU Max Series (formerly Sapphire Rapids HBM)
- Intel Core Ultra processors (formerly Meteor Lake, Arrow Lake, and Lunar Lake)
- Intel Xeon 6 processors (formerly Sierra Forest and Granite Rapids)
- AArch64 architecture
- Arm Neoverse(TM) N1 and V1 processors
On a CPU based on Intel 64 or on AMD64 architecture, oneDNN detects the instruction set architecture (ISA) at runtime and uses just-in-time (JIT) code generation to deploy the code optimized for the latest supported ISA. Future ISAs may have initial support in the library disabled by default and require the use of run-time controls to enable them. See CPU dispatcher control for more details.
WARNING
On macOS, applications that use oneDNN may need to request special entitlements if they use the hardened runtime. See the Linking Guide for more details.
The library is optimized for the following GPUs:
- Intel Graphics for 11th-14th Generation Intel Core Processors
- Intel Iris Xe MAX Graphics (formerly DG1)
- Intel Arc(TM) graphics (formerly Alchemist)
- Intel Data Center GPU Flex Series (formerly Arctic Sound)
- Intel Data Center GPU Max Series (formerly Ponte Vecchio)
- Intel Graphics and Intel Arc graphics for Intel Core Ultra processors (formerly Meteor Lake, Arrow Lake and Lunar Lake)
- future Intel Arc graphics (code name Battlemage)
oneDNN supports systems meeting the following requirements:
- Operating system with Intel 64 / Arm 64 / Power / IBMz architecture support
- C++ compiler with C++11 standard support
- CMake 2.8.12 or later
The following tools are required to build oneDNN documentation:
- Doxygen 1.8.5 or later
- Doxyrest 2.1.2 or later
- Sphinx 4.0.2 or later
- sphinx-book-theme 0.0.41 or later
Configurations of CPU and GPU engines may introduce additional build time dependencies.
oneDNN CPU engine is used to execute primitives on Intel Architecture Processors, 64-bit Arm Architecture (AArch64) processors, 64-bit Power ISA (PPC64) processors, IBMz (s390x), and compatible devices.
The CPU engine is built by default but can be disabled at build time by setting
DNNL_CPU_RUNTIME to NONE. In this case, GPU engine must be enabled.
The CPU engine can be configured to use the OpenMP, TBB or SYCL runtime.
The following additional requirements apply:
- OpenMP runtime requires C++ compiler with OpenMP 2.0 or later standard support
- TBB runtime requires Threading Building Blocks (TBB) 2017 or later.
- SYCL runtime requires
Some implementations rely on OpenMP 4.0 SIMD extensions. For the best performance results on Intel Architecture Processors we recommend using the Intel C++ Compiler.
On a CPU based on Arm AArch64 architecture, oneDNN CPU engine can be built with Arm Compute Library (ACL) integration. ACL is an open-source library for machine learning applications and provides AArch64 optimized implementations of core functions. This functionality currently requires that ACL is downloaded and built separately. See Build from Source section of the Developer Guide for details. oneDNN only supports Compute Library versions 24.09 or later.
Intel Processor Graphics and Xe Architecture graphics are supported by the oneDNN GPU engine. The GPU engine is disabled in the default build configuration. The following additional requirements apply when GPU engine is enabled:
- OpenCL runtime requires
- OpenCL* runtime library (OpenCL version 1.2 or later)
- OpenCL driver (with kernel language support for OpenCL C 2.0 or later) with Intel subgroups and USM extensions support
- SYCL runtime requires
- Intel oneAPI DPC++/C++ Compiler
- OpenCL runtime library (OpenCL version 3.0 or later)
- oneAPI Level Zero
- SYCL runtime with NVIDIA GPU support requires
- oneAPI DPC++ Compiler with support for CUDA or oneAPI for NVIDIA GPUs
- NVIDIA CUDA* driver
- cuBLAS 10.1 or later
- cuDNN 7.6 or later
- SYCL runtime with AMD GPU support requires
- oneAPI DPC++ Compiler with support for HIP AMD or oneAPI for AMD GPUs
- AMD ROCm version 5.3 or later
- MIOpen version 2.18 or later (optional if AMD ROCm includes the required version of MIOpen)
- rocBLAS version 2.45.0 or later (optional if AMD ROCm includes the required version of rocBLAS)
- SYCL runtime with a generic GPU support requires
- oneAPI DPC++/C++ Compiler that supports the target GPU. Refer to the generic GPU vendor readme for more information.
WARNING
Linux will reset GPU when kernel runtime exceeds several seconds. The user can prevent this behavior by disabling hangcheck for Intel GPU driver. Windows has built-in timeout detection and recovery mechanism that results in similar behavior. The user can prevent this behavior by increasing the TdrDelay value.
WARNING
NVIDIA GPU support is experimental. General information, build instructions, and implementation limitations are available in the NVIDIA backend readme.
WARNING
AMD GPU support is experimental. General information, build instructions, and implementation limitations are available in the AMD backend readme.
When oneDNN is built from source, the library runtime dependencies and specific versions are defined by the build environment.
Common dependencies:
- GNU C Library (
libc.so) - GNU Standard C++ Library v3 (
libstdc++.so) - Dynamic Linking Library (
libdl.so) - C Math Library (
libm.so) - POSIX Threads Library (
libpthread.so)
Runtime-specific dependencies:
| Runtime configuration | Compiler | Dependency |
|---|---|---|
DNNL_CPU_RUNTIME=OMP |
GCC | GNU OpenMP runtime (libgomp.so) |
DNNL_CPU_RUNTIME=OMP |
Intel C/C++ Compiler | Intel OpenMP runtime (libiomp5.so) |
DNNL_CPU_RUNTIME=OMP |
Clang | Intel OpenMP runtime (libiomp5.so) |
DNNL_CPU_RUNTIME=TBB |
any | TBB (libtbb.so) |
DNNL_CPU_RUNTIME=SYCL |
Intel oneAPI DPC++ Compiler | Intel oneAPI DPC++ Compiler runtime (libsycl.so), TBB (libtbb.so), OpenCL loader (libOpenCL.so) |
DNNL_GPU_RUNTIME=OCL |
any | OpenCL loader (libOpenCL.so) |
DNNL_GPU_RUNTIME=SYCL |
Intel oneAPI DPC++ Compiler | Intel oneAPI DPC++ Compiler runtime (libsycl.so), OpenCL loader (libOpenCL.so), oneAPI Level Zero loader (libze_loader.so) |
Common dependencies:
- Microsoft Visual C++ Redistributable (
msvcrt.dll)
Runtime-specific dependencies:
| Runtime configuration | Compiler | Dependency |
|---|---|---|
DNNL_CPU_RUNTIME=OMP |
Microsoft Visual C++ Compiler | No additional requirements |
DNNL_CPU_RUNTIME=OMP |
Intel C/C++ Compiler | Intel OpenMP runtime (iomp5.dll) |
DNNL_CPU_RUNTIME=TBB |
any | TBB (tbb.dll) |
DNNL_CPU_RUNTIME=SYCL |
Intel oneAPI DPC++ Compiler | Intel oneAPI DPC++ Compiler runtime (sycl.dll), TBB (tbb.dll), OpenCL loader (OpenCL.dll) |
DNNL_GPU_RUNTIME=OCL |
any | OpenCL loader (OpenCL.dll) |
DNNL_GPU_RUNTIME=SYCL |
Intel oneAPI DPC++ Compiler | Intel oneAPI DPC++ Compiler runtime (sycl.dll), OpenCL loader (OpenCL.dll), oneAPI Level Zero loader (ze_loader.dll) |
Common dependencies:
- System C/C++ runtime (
libc++.dylib,libSystem.dylib)
Runtime-specific dependencies:
| Runtime configuration | Compiler | Dependency |
|---|---|---|
DNNL_CPU_RUNTIME=OMP |
Intel C/C++ Compiler | Intel OpenMP runtime (libiomp5.dylib) |
DNNL_CPU_RUNTIME=TBB |
any | TBB (libtbb.dylib) |
CPU engine was validated on RedHat* Enterprise Linux 8 with
- GNU Compiler Collection 5.4, 6.1, 7.2, 8.1, 9.1, 11.1, 11.3
- Clang* 7.1, 8.0, 9.0, 14.0.6
- Intel oneAPI DPC++/C++ Compiler 2024.0
on Windows Server* 2019 with
- Microsoft Visual Studio 2022
- Intel oneAPI DPC++/C++ Compiler 2024.0
on macOS 11 (Big Sur) with
- Apple LLVM version 13.0
on Ubuntu 20.04 AArch64 with
- GNU Compiler Collection 7.0, 8.0, 9.0, 10.0
- Clang* 9.0, 17.0
- Arm Compiler for Linux 24.04
- Arm Compute Library (ACL) built for armv8-a arch, latest stable version available at the time of release
GPU engine was validated on Ubuntu* 22.04 with
- GNU Compiler Collection 7.2, 8.1, and 9.1
- Clang 7.1, 8.0, 9.0
- Intel oneAPI DPC++/C++ Compiler 2024.0
- Intel Software for General Purpose GPU capabilities latest stable version available at the time of release
on Windows Server 2019 with
- Microsoft Visual Studio 2022
- Intel oneAPI DPC++/C++ Compiler 2024.0
- Intel Arc & Iris Xe Graphics Driver latest stable version available at the time of release
- Apache* MXNet
- Apache SINGA
- DeepLearning4J*
- Flashlight*
- Korali
- MATLAB* Deep Learning Toolbox
- ONNX Runtime
- OpenVINO(TM) toolkit
- PaddlePaddle*
- PyTorch*. Intel GPU support and additional optimizations are available with Intel Extension for PyTorch.
- Tensorflow*. Intel GPU support and additional optimizations are available with Intel Extension for Tensorflow.
Submit questions, feature requests, and bug reports on the GitHub issues page.
You can also contact oneDNN developers via UXL Foundation Slack using #onednn channel.
oneDNN project is governed by the UXL Foundation and you can get involved in this project in multiple ways. It is possible to join the AI Special Interest Group (SIG) meetings where the groups discuss and demonstrate work using this project. Members can also join the Open Source and Specification Working Group meetings.
You can also join the mailing lists for the UXL Foundation to be informed of when meetings are happening and receive the latest information and discussions.
We welcome community contributions to oneDNN. You can find the oneDNN release schedule and work already in progress towards future milestones in Github's Milestones section. If you are looking for a specific task to start, consider selecting from issues that are marked with the help wanted label.
If you have an idea on how to improve the library:
- For changes impacting the public API or library overall, such as adding new primitives or changes to the architecture, submit an RFC pull request.
- Ensure that the changes are consistent with the code contribution guidelines and coding standards.
- Ensure that you can build the product and run all the examples with your patch.
- Submit a pull request.
For additional details, see contribution guidelines. You can also contact oneDNN developers and maintainers via UXL Foundation Slack using #onednn channel.
This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.
oneDNN is licensed under Apache License Version 2.0. Refer to the "LICENSE" file for the full license text and copyright notice.
This distribution includes third party software governed by separate license terms.
3-clause BSD license:
2-clause BSD license:
Apache License Version 2.0:
Boost Software License, Version 1.0:
MIT License:
- Intel Graphics Compute Runtime for oneAPI Level Zero and OpenCL Driver
- Intel Graphics Compiler
- oneAPI Level Zero
- Doxyrest
- Intel Metrics Discovery Application Programming Interface
- spdlog
This third party software, even if included with the distribution of the Intel software, may be governed by separate license terms, including without limitation, third party license terms, other Intel software license terms, and open source software license terms. These separate license terms govern your use of the third party programs as set forth in the "THIRD-PARTY-PROGRAMS" file.
Security Policy outlines our guidelines and procedures for ensuring the highest level of Security and trust for our users who consume oneDNN.
Intel, the Intel logo, Arc, Intel Atom, Intel Core, Iris, OpenVINO, the OpenVINO logo, Pentium, VTune, and Xeon are trademarks of Intel Corporation or its subsidiaries.
Arm and Neoverse are trademarks, or registered trademarks of Arm Ltd.
* Other names and brands may be claimed as the property of others.
Microsoft, Windows, and the Windows logo are trademarks, or registered trademarks of Microsoft Corporation in the United States and/or other countries.
OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos.
(C) Intel Corporation