[OpenBLAS] Update to v0.3.26 #7911
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giordano
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julia 💜 ❤️ 💚
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With OpenMathLib/OpenBLAS#4394 Apple M series CPUs use NeoverseN1 kernels when OpenBLAS is compiled with julia> using LinearAlgebra, BenchmarkTools
julia> peakflops()
1.7049262964078418e11
julia> x = randn(1_000); y = randn(size(x));
julia> @btime dot($x, $y);
402.705 ns (0 allocations: 0 bytes)with OpenBLAS v0.3.26: julia> using LinearAlgebra, BenchmarkTools
julia> peakflops()
1.8042546290642157e11
julia> x = randn(1_000); y = randn(size(x));
julia> @btime dot($x, $y);
143.777 ns (0 allocations: 0 bytes)Up to OpenBLAS v0.3.25 you can get similar performance on Apple Silicon by exporting the environment variable OPENBLAS_CORETYPE=NEOVERSEN1 |
giordano
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Memo to self: * update version number in `stdlib/OpenBLAS_jll/Project.toml` * update version number and sha in `deps/openblas.version` * refresh checksums with `make -f contrib/refresh_checksums.mk -j openblas` In the [release notes of v0.3.26](https://github.com/OpenMathLib/OpenBLAS/releases/tag/v0.3.26) one of the more interesting point is perhaps > included support for Apple M1 and newer targets in DYNAMIC_ARCH builds Quoting from JuliaPackaging/Yggdrasil#7911 (comment) > Some quick benchmarks on M1: with OpenBLAS 0.3.25: > ```julia > julia> using LinearAlgebra, BenchmarkTools > > julia> peakflops() > 1.7049262964078418e11 > > julia> x = randn(1_000); y = randn(size(x)); > > julia> @Btime dot($x, $y); > 402.705 ns (0 allocations: 0 bytes) > ``` > with OpenBLAS v0.3.26: > ```julia > julia> using LinearAlgebra, BenchmarkTools > > julia> peakflops() > 1.8042546290642157e11 > > julia> x = randn(1_000); y = randn(size(x)); > > julia> @Btime dot($x, $y); > 143.777 ns (0 allocations: 0 bytes) > ``` > Up to OpenBLAS v0.3.25 you can get similar performance on Apple Silicon by exporting the environment variable > ```sh > OPENBLAS_CORETYPE=NEOVERSEN1 > ```
MichelJuillard
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Jan 8, 2024
KristofferC
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Jan 24, 2024
Memo to self: * update version number in `stdlib/OpenBLAS_jll/Project.toml` * update version number and sha in `deps/openblas.version` * refresh checksums with `make -f contrib/refresh_checksums.mk -j openblas` In the [release notes of v0.3.26](https://github.com/OpenMathLib/OpenBLAS/releases/tag/v0.3.26) one of the more interesting point is perhaps > included support for Apple M1 and newer targets in DYNAMIC_ARCH builds Quoting from JuliaPackaging/Yggdrasil#7911 (comment) > Some quick benchmarks on M1: with OpenBLAS 0.3.25: > ```julia > julia> using LinearAlgebra, BenchmarkTools > > julia> peakflops() > 1.7049262964078418e11 > > julia> x = randn(1_000); y = randn(size(x)); > > julia> @Btime dot($x, $y); > 402.705 ns (0 allocations: 0 bytes) > ``` > with OpenBLAS v0.3.26: > ```julia > julia> using LinearAlgebra, BenchmarkTools > > julia> peakflops() > 1.8042546290642157e11 > > julia> x = randn(1_000); y = randn(size(x)); > > julia> @Btime dot($x, $y); > 143.777 ns (0 allocations: 0 bytes) > ``` > Up to OpenBLAS v0.3.25 you can get similar performance on Apple Silicon by exporting the environment variable > ```sh > OPENBLAS_CORETYPE=NEOVERSEN1 > ``` (cherry picked from commit b682592)
grasph
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Jul 1, 2024
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