# Current OpenBLAS Versions (January 2022) do not support Intel gen 11 performantly?

**URL:** <https://discourse.julialang.org/t/current-openblas-versions-january-2022-do-not-support-intel-gen-11-performantly/75104>\
**Category:** Performance\
**Tags:** linearalgebra\
**Created:** [January 24, 2022, 6:06am UTC](https://discourse.julialang.org/t/current-openblas-versions-january-2022-do-not-support-intel-gen-11-performantly/75104 "2022-01-24T06:06:15Z")\
**Posts on this page:** 1\
**Showing post:** 30

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**Author:** ![fgerick](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/fgerick/32/13228_2.png) [@fgerick](https://discourse.julialang.org/u/fgerick)\
**Post date:** [January 27, 2022, 7:49am UTC](https://discourse.julialang.org/t/current-openblas-versions-january-2022-do-not-support-intel-gen-11-performantly/75104/30 "2022-01-27T07:49:23Z")

</div>

Is there something that needs to be taken into account or configured for AMD Milan processors? I just ran the same code on a 2x24 core AMD EPYC 7443 machine and got this result:

```julia
julia> Threads.nthreads()
96

julia> using LinearAlgebra; BLAS.set_num_threads(Sys.CPU_THREADS ÷ 2);

julia> A = rand(10_000,10_000); B = similar(A);

julia> @time mul!(B, A, A);
  3.391364 seconds (2.49 M allocations: 124.344 MiB, 12.61% compilation time)

julia> @time mul!(B, A, A);
  3.178868 seconds

julia> using MKL

julia> @time mul!(B, A, A);
  2.854096 seconds

julia> @time mul!(B, A, A);
  2.724407 seconds

julia> using Octavian

julia> @time matmul!(B, A, A);
 14.762420 seconds (28.56 M allocations: 1.495 GiB, 1.96% gc time, 80.99% compilation time)

julia> @time matmul!(B, A, A);
  3.115852 seconds

julia> versioninfo()
Julia Version 1.8.0-DEV.1405
Commit 2010d95d8a (2022-01-26 17:41 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: AMD EPYC 7443 24-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-13.0.0 (ORCJIT, znver3)

```

Shouldn’t the 7513 and 7443 be quite comparable, despite the 8 more cores?

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