# How to circumvent Intel's AMD discrimination in MKL from v1.7 onwards?

**URL:** <https://discourse.julialang.org/t/how-to-circumvent-intels-amd-discrimination-in-mkl-from-v1-7-onwards/105075>\
**Category:** General Usage\
**Tags:** mkl, linearalgebra\
**Created:** [October 17, 2023, 2:41pm UTC](https://discourse.julialang.org/t/how-to-circumvent-intels-amd-discrimination-in-mkl-from-v1-7-onwards/105075 "2023-10-17T14:41:55Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**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:** [October 17, 2023, 2:41pm UTC](https://discourse.julialang.org/t/how-to-circumvent-intels-amd-discrimination-in-mkl-from-v1-7-onwards/105075/1 "2023-10-17T14:41:55Z")

</div>

Hi everyone,  
I’ve been ending up having to compute a lot of large dense non-hermitian eigenproblems on an HPC cluster that uses AMD EPYC Milan CPUs. I thought I could use Intel MKL, since it usually does a better job for `zgeev` (and presumably many other LAPACK routines) than OpenBLAS.

It’s known for a while that Intel is actively trying to slow down AMD CPUs when using their MKL library. First, there was the `MKL_DEBUG_CPU_TYPE` flag, which then was removed by Intel in the later versions of MKL. Fortunately, there exists a workaround, preloading a fake library using `LD_PRELOAD` [as described here](https://danieldk.eu/Posts/2020-08-31-MKL-Zen.html). Unfortunately, this trick does not work for Julia, as discussed in [a related post](https://discourse.julialang.org/t/discussion-of-ccall-function-library/53887).

I illustrate the potential performance difference:

We can trick MKL using Julia v1.6.7 build from source and `USE_INTEL_MKL=1`. Then, using the hack with `LD_PRELOAD` gives

```julia
julia> using LinearAlgebra

julia> BLAS.set_num_threads(8)

julia> n=200; a = randn(ComplexF64,n,n);

julia> @time eigen(a);
  0.835277 seconds (1.67 M allocations: 102.661 MiB, 1.57% gc time, 78.93% compilation time)

julia> n=2000; a = randn(ComplexF64,n,n);

julia> @time eigen(a);
  5.833620 seconds (26 allocations: 133.959 MiB, 0.40% gc time)

```

without the `LD_PRELOAD`:

```julia
julia> using LinearAlgebra

julia> BLAS.set_num_threads(8)

julia> n=200; a = randn(ComplexF64,n,n);

julia> @time eigen(a);
  0.740193 seconds (1.67 M allocations: 102.664 MiB, 2.00% gc time, 90.19% compilation time)

julia> n=2000; a = randn(ComplexF64,n,n);

julia> @time eigen(a);
 10.117982 seconds (26 allocations: 133.959 MiB, 0.21% gc time)

```

And using Julia v1.9 with `MKL.jl` (with or without `LD_PRELOAD`):

```julia
julia> using MKL

julia> using LinearAlgebra

julia> BLAS.set_num_threads(8)

julia> n=200; a = randn(ComplexF64,n,n);

julia> @time eigen(a);
  1.192825 seconds (1.67 M allocations: 109.709 MiB, 6.84% gc time, 89.60% compilation time)

julia> n=2000; a = randn(ComplexF64,n,n);

julia> @time eigen(a);
  9.663940 seconds (26 allocations: 133.974 MiB, 0.08% gc time)

```

For comleteness Julia v1.9 with OpenBLAS:

```julia
julia> using LinearAlgebra

julia> BLAS.set_num_threads(8)

julia> n=200; a = randn(ComplexF64,n,n);

julia> @time eigen(a);
  1.312937 seconds (1.67 M allocations: 109.224 MiB, 8.13% gc time, 91.58% compilation time)

julia> n=2000; a = randn(ComplexF64,n,n);

julia> @time eigen(a);
  8.914101 seconds (26 allocations: 126.161 MiB, 0.10% gc time)

```

Now, since I would like to avoid using Julia 1.6.7 and since `MKL_jll.jl` exists, what can we do to make the best use of MKL on AMD CPUs? Is there a simple way of changing `MKL.jl` or `MKL_jll.jl` to make either the `LD_PRELOAD` hack work, or is there a julia internal solution?

I hope I haven’t missed any discussions on this somewhere.

---

<div class="post-metadata">

**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:** [October 19, 2023, 1:54pm UTC](https://discourse.julialang.org/t/how-to-circumvent-intels-amd-discrimination-in-mkl-from-v1-7-onwards/105075/2 "2023-10-19T13:54:08Z")

</div>

Okay I actually found a hacky solution myself for now:

```julia
using MKL
mklpath = dirname(MKL.MKL_jll.libmkl_rt_path)

cd(mklpath)

rm("libmkl_rt.so")
rm("libmkl_core.so")

write("libamdmkl.c","int mkl_serv_intel_cpu_true() {return 1;}")
run(`gcc -shared -o libmkl_core.so -Wl,-rpath=''\$ORIGIN'' libamdmkl.c libmkl_core.so.2`)
run(`gcc -shared -o libmkl_rt.so -Wl,-rpath=''\$ORIGIN'' libamdmkl.c libmkl_rt.so.2`)
rm("libamdmkl.c")

```

The original `libmkl_rt.so` and `libmkl_core.so` are just softlinks to `libmkl_rt.so.2` and `libmkl_core.so.2`, so we don’t mess up things too much (for those that are worried).  
I guess there are many reasons not to do this or cases where it fails, but it works for my system and I guess many similar ones as well!

EDIT: ~~actually this doesn’t work properly. It doesn’t link properly to the `libmkl_rt.so.2` library, but I had another MKL version in the library path, so then this hack works. I don’t know how to properly link the library though without relative paths.~~  
I have updated the code to make it work for relative paths. Maybe someone will come up with something more elegant!

---

<div class="post-metadata">

**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:** [May 16, 2025, 3:55pm UTC](https://discourse.julialang.org/t/how-to-circumvent-intels-amd-discrimination-in-mkl-from-v1-7-onwards/105075/3 "2025-05-16T15:55:32Z")

</div>

Make sure to

```julia
import Pkg; Pkg.pin(name="MKL_jll", version="2024.1.0")

```

since it seems like Intel caught up on this solution to AMD MKL performance.