From b4d2e481594fd046df2bc655a81fa503919f5f82 Mon Sep 17 00:00:00 2001 From: Chris Peel Date: Wed, 23 Aug 2023 17:40:13 -0700 Subject: [PATCH] Updated performance figure and README --- Project.toml | 2 +- README.md | 74 +++++------------- docs/src/assets/perfVsDataType.png | Bin 57433 -> 55965 bytes docs/src/assets/timeVdim_25bitsInt64.png | Bin 54531 -> 0 bytes docs/src/assets/timeVsmallest_25bitsInt64.png | Bin 47538 -> 0 bytes docs/src/functions.md | 1 + docs/src/index.md | 67 ++++------------ src/l2.jl | 20 ++--- test/lrtest.jl | 3 +- test/perftest.jl | 5 +- 10 files changed, 52 insertions(+), 120 deletions(-) delete mode 100644 docs/src/assets/timeVdim_25bitsInt64.png delete mode 100644 docs/src/assets/timeVsmallest_25bitsInt64.png diff --git a/Project.toml b/Project.toml index 0bcd17d..a1e34ce 100644 --- a/Project.toml +++ b/Project.toml @@ -2,7 +2,7 @@ name = "LLLplus" uuid = "142c1900-a1c3-58ae-a66d-b187f9ca6423" keywords = ["lattice reduction", "lattice basis reduction", "SVP", "shortest vector problem", "CVP", "closest vector problem", "LLL", "Lenstra-Lenstra-Lovász", "Seysen", "Brun", "VBLAST", "subset-sum problem", "Lagarias-Odlyzko", "Bailey–Borwein–Plouffe formula"] license = "MIT" -version = "1.3.4" +version = "1.3.5" [deps] DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab" diff --git a/README.md b/README.md index ec2f065..6560ca5 100644 --- a/README.md +++ b/README.md @@ -3,10 +3,9 @@ [![Build Status](https://github.com/christianpeel/LLLplus.jl/workflows/CI/badge.svg)](https://github.com/christianpeel/LLLplus.jl/actions) [![](https://img.shields.io/badge/docs-devel-blue.svg)](https://christianpeel.github.io/LLLplus.jl/dev) -LLLplus provides lattice tools such as -Lenstra-Lenstra-Lovász (LLL) lattice reduction which are of practical and -theoretical use in cryptography, digital communication, integer -programming, and more. +LLLplus provides lattice tools such as Lenstra-Lenstra-Lovász (LLL) +lattice reduction which are of practical and theoretical use in +cryptography, digital communication, integer programming, and more. This package is experimental and not a robust tool; use at your own risk :-) @@ -26,7 +25,7 @@ functions are also included; see the `subsetsum`, `minimalpolynomial`, `integerfeasibility`, `rationalapprox`, and `spigotBBP` functions.
- Examples (click for details) + Basic Examples (click for details)

Each function contains documentation and examples available via Julia's @@ -45,10 +44,10 @@ using LLLplus # do lattice reduction on a matrix with randn entries N = 40; H = randn(N,N); -B,T = brun(H); -B,T = lll(H); -B,T = seysen(H); -B,T = hkz(H); +Bbrun,_ = brun(H); +Blll,_ = lll(H); +Bseysen,_ = seysen(H); +Bhkz,_ = hkz(H); # check out the CVP solver Q,Rtmp=qr(H); R = UpperTriangular(Rtmp); @@ -64,54 +63,18 @@ sum(abs.(u-uhat))

Execution Time results (click for details)

-In the first test we compare several LLL functions: the `lll` function from LLLplus, the -`l2avx` function in the `src\l2.jl` file in LLLplus, the -`lll_with_transform` function from -[Nemo.jl](https://github.com/Nemocas/Nemo.jl) (which uses FLINT), and -the `lll_reduction` function from -[fplll](https://github.com/fplll/fplll). Nemo is written by number -theorists, while fplll is written -by lattice cryptanalysis academics; they are good benchmarks against which to compare. -We first show how the execution time varies as the basis (matrix) size -varies over [4 8 16 32 64]. For each matrix size, 20 random bases are -generated using fplll's `gen_qary` function with depth of 25 bits, -with the average execution time shown; the `eltype` is `Int64` except -for NEMO, which can only use GMP (its own `BigInt`); in all cases the -`δ=.99`. The vertical axis shows execution time on a logarithmic -scale; the x-axis is also logarithmic. -The `lll` function is slower, while `l2avx` is similar to -fplll. Though not shown, using bases from `gen_qary` with bit depth of -45 gives fplll a larger advantage. Though the LLLplus functions are -not the fastest, they are in the same ballpark as the C and -C++ tools; if this package gets more users, we'll spend more time on -speed :-) This figure was generated using code in `test/timeLLLs.jl`. - -![Time vs basis size](docs/src/assets/timeVdim_25bitsInt64.png) - -One additional question that could arise when looking at the plot above is what -the quality of the basis is. In the next plot we show execution time -vs the norm of the first vector in the reduced basis, this first -vector is typically the smallest; its norm is an rough indication of -the quality of the reduced basis. We show results averaged over 20 -random bases from `gen_qary` with depth `25` bits, this time with the -dimension fixed at `32`. The curve is created by varying the `δ` -parameter from `.29` to `.99` in steps of `.2`; the larger times and -smaller norms correspond to the largest `δ` values. Though the `l2avx` -function is competitive with fplll in this case, in most cases -the fplll code is faster. - -![Time vs reduction quality](docs/src/assets/timeVsmallest_25bitsInt64.png) - -Finally, we show execution time for several built-in -datatypes (Int32, Int64, Int128, Float32, Float64, BitInt, and -BigFloat) as well as type from external packages (Float128 from +To give a flavor of the behavior of the functions in LLLplus, +we show execution time for several built-in datatypes (Int32, +Int64, Int128, Float32, Float64, BitInt, and BigFloat) as well as type +from external packages (Float128 from [Quadmath.jl](https://github.com/JuliaMath/Quadmath.jl) and Double64 from [DoubleFloat.jl](https://github.com/JuliaMath/DoubleFloats.jl)) -which are used to -generate 60 16x16 matrices, over which execution time for the -lattice reduction techniques is averaged. The vertical axis is a -logarithmic representation of execution time as in the previous -figure. This figure was generated using code in `test/perftest.jl`. +which are used to generate 100 16x16 matrices with elements uniformly +distributed over `-100` to `100`. The figure shows average execution +time when using these matrices as input lattice bases for several +functions from LLLplus. See `test/perftest.jl` for the code to +regenerate the figure and for another line of code that generates a +figure of execution time versus basis dimension. ![Time vs data type](docs/src/assets/perfVsDataType.png) @@ -135,4 +98,5 @@ number-theoretic problems the [Nemo.jl](https://github.com/Nemocas/Nemo.jl) package is appropriate; it uses the [FLINT](http://flintlib.org/) C library to do LLL reduction on Nemo-specific data types. 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a/docs/src/index.md b/docs/src/index.md index 51614de..477faf3 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -5,14 +5,13 @@ CurrentModule = LLLplus ``` LLLplus provides lattice tools such as -[Lenstra-Lenstra-Lovász](https://en.wikipedia.org/wiki/Lenstra%E2%80%93Lenstra%E2%80%93Lov%C3%A1sz_lattice_basis_reduction_algorithm) -(LLL) lattice reduction which are of practical and +Lenstra-Lenstra-Lovász (LLL) lattice reduction which are of practical and theoretical use in cryptography, digital communication, integer programming, and more. This package is experimental and not a robust tool; use at your own risk :-) -LLLplus has functions for LLL, +LLLplus has functions for [LLL](https://en.wikipedia.org/wiki/Lenstra%E2%80%93Lenstra%E2%80%93Lov%C3%A1sz_lattice_basis_reduction_algorithm), [Seysen](http://link.springer.com/article/10.1007%2FBF01202355), and [Hermite-Korkine-Zolotarev](http://www.cas.mcmaster.ca/~qiao/publications/ZQW11.pdf) lattice reduction @@ -46,10 +45,10 @@ using LLLplus # do lattice reduction on a matrix with randn entries N = 40; H = randn(N,N); -B,T = brun(H); -B,T = lll(H); -B,T = seysen(H); -B,T = hkz(H); +Bbrun,_ = brun(H); +Blll,_ = lll(H); +Bseysen,_ = seysen(H); +Bhkz,_ = hkz(H); # check out the CVP solver Q,Rtmp=qr(H); R = UpperTriangular(Rtmp); @@ -61,52 +60,18 @@ sum(abs.(u-uhat)) ### Execution Time results -In the first test we compare several LLL functions: the `lll` function from LLLplus, the -`l2avx` function in the `src\l2.jl` file in LLLplus, the -`lll_with_transform` function from Nemo (which uses FLINT), and the -`lll_reduction` function from fplll. Nemo is written by number -theorists, while fplll is written -by lattice cryptanalysis academics; they are good benchmarks against which to compare. We -first show how the execution time varies as the basis (matrix) size -varies over [4 8 16 32 64]. For each matrix size, 20 random bases -are generated using fplll's `gen_qary` function with depth of 25 -bits, with the average execution time shown; the `eltype` is `Int64` -except for NEMO, which uses GMP (its own `BigInt`); in all cases the -`δ=.99`. The vertical axis shows -execution time on a logarithmic scale; the x-axis is also -logarithmic. The generally linear nature of the LLL curves supports -the polynomial-time nature of the algorithm. The `LLLplus.lll` -function is slower, while `l2avx` is similar to fplll. Though not -shown, using bases from `gen_qary` with bit depth of 45 gives fplll -a larger advantage. This figure was generated using code in -`test/timeLLLs.jl`. - -![Time vs basis size](assets/timeVdim_25bitsInt64.png) - -One additional question that could arise when looking at the plot above is what -the quality of the basis is. In the next plot we show execution time -vs the norm of the first vector in the reduced basis, this first -vector is typically the smallest; its norm is an rough indication of -the quality of the reduced basis. We show results averaged over 20 -random bases from `gen_qary` with depth `25` bits, this time with the -dimension fixed at `32`. The curve is created by varying the `δ` -parameter from `.29` to `.99` in steps of `.2`; the larger times and -smaller norms correspond to the largest `δ` values. Though the `l2avx` -function is competitive with fplll in this case, in most cases -the fplll code is faster. - -![Time vs reduction quality](assets/timeVsmallest_25bitsInt64.png) - -Finally, we show execution time for several built-in -datatypes (Int32, Int64, Int128, Float32, Float64, BitInt, and -BigFloat) as well as type from external packages (Float128 from +To give a flavor of the behavior of the functions in LLLplus, +we show execution time for several built-in datatypes (Int32, +Int64, Int128, Float32, Float64, BitInt, and BigFloat) as well as type +from external packages (Float128 from [Quadmath.jl](https://github.com/JuliaMath/Quadmath.jl) and Double64 from [DoubleFloat.jl](https://github.com/JuliaMath/DoubleFloats.jl)) -which are used to -generate 60 16x16 matrices, over which execution time for the -lattice reduction techniques is averaged. The vertical axis is a -logarithmic representation of execution time as in the previous -figure. This figure was generated using code in `test/perftest.jl`. +which are used to generate 100 16x16 matrices with elements uniformly +distributed over `-100` to `100`. The figure shows average execution +time when using these matrices as input lattice bases for several +functions from LLLplus. See `test/perftest.jl` for the code to +regenerate the figure and for another line of code that generates a +figure of execution time versus basis dimension. ![Time vs data type](assets/perfVsDataType.png) diff --git a/src/l2.jl b/src/l2.jl index e230303..a3276e1 100644 --- a/src/l2.jl +++ b/src/l2.jl @@ -180,28 +180,28 @@ function lazysizereduction!(ηb,κ,B,G,r,μ,s,X,n,d,Tg) end #= """ - B = l2avx(H::AbstractArray{Td,2},TG::Type{Tg},δ=.75,η=.51) where + B = l2turbo(H::AbstractArray{Td,2},TG::Type{Tg},δ=.75,η=.51) where {Td<:Number,Tg<:Number} -A version of the `l2` function with a few calls to the `@avx` macro +A version of the `l2` function with a few calls to the `@turbo` macro from the `LoopVectorization.jl` package. See the `l2` help text # Examples ```julia julia> using LLLplus julia> using LoopVectorization -julia> H= [1 2; 3 4];B = l2avx(H) -┌ Warning: l2avx is in a raw (alpha) state and may change. See the help text. +julia> H= [1 2; 3 4];B = l2turbo(H) +┌ Warning: l2turbo is in a raw (alpha) state and may change. See the help text. └ @ LLLplus ~/shared/LLLplus/src/l2.jl:42 2×2 Matrix{Int64}: 1 -1 1 1 ``` """ -function l2avx(H::AbstractArray{Td,2},TG::Type{Tg}=Td,δ=.75,η=.51) where +function l2turbo(H::AbstractArray{Td,2},TG::Type{Tg}=Td,δ=.75,η=.51) where {Td<:Number,Tg<:Number} - @warn "l2avx is in a raw (alpha) state and may change. See the help text." maxlog=1 + @warn "l2turbo is in a raw (alpha) state and may change. See the help text." maxlog=1 if !(0.25 < δ < 1.0) error("δ must be between 1/4 and 1."); @@ -218,7 +218,7 @@ function l2avx(H::AbstractArray{Td,2},TG::Type{Tg}=Td,δ=.75,η=.51) where ϵ = .001 # ϵ = eps(Td) may be too small C = ϵ if Tf <: Complex - @error "`l2avx` does not handle complex data; try `lll`." + @error "`l2turbo` does not handle complex data; try `lll`." return end Tfe = real(Tf) @@ -268,7 +268,7 @@ function l2avx(H::AbstractArray{Td,2},TG::Type{Tg}=Td,δ=.75,η=.51) where B[:,κ] .= bκp if κ1 yscale=:log10; xval = N; xlab = "Matrix Size"; + xtickStrs = [string(ix) for ix ∈ N] tstr = @sprintf("Ns=%d,Type=%s,dist=%s", Ns,string(dataType[1]),distType); elseif length(L)>1 diff --git a/test/perftest.jl b/test/perftest.jl index b10835c..6adffbf 100644 --- a/test/perftest.jl +++ b/test/perftest.jl @@ -7,7 +7,6 @@ using DoubleFloats using Quadmath gr(); -#include("../src/l2.jl") include("lrtest.jl") # getIntType is used to indicate what type we want the unimodular integer @@ -18,8 +17,10 @@ import LLLplus.getIntType getIntType(Td::Type{Tr}) where {Tr<:Float128} = Int128 getIntType(Td::Type{Tr}) where {Tr<:Double64} = Int128 -lrtest(40,2 .^[3],[100],[Int32,Int64,Int128,Float32,Float64,Double64,Float128,BigInt,BigFloat],"rand") +lrtest(100,2 .^[4],[100],[Int32,Int64,Int128,Float32,Float64,Double64,Float128,BigInt,BigFloat],"rand") savefig("perfVsDataType.png") +#display(current()) # lrtest(40,2 .^[1:8;],[1],[Float64],"randn") # savefig("perfVsNfloat64.png") +