My lab has some AMD EPYC 7713 servers. We bought them because some people in the group run programs with very high CPU load (I don’t know what kind of load it is, or why it can’t run on the GPU, and I don’t have the energy to help everyone solve it one by one). AMD processors with their many cores are a great fit for this kind of demand.

But as nice as AMD processors are, using them in a deep-learning lab brings an extra problem: the numpy and PyTorch installed by Anaconda both use MKL as their BLAS implementation by default, and MKL’s library functions are also the hotspots of most high-CPU-load programs. However, MKL checks whether it is running on an Intel CPU, and if not, the optimizations have no effect.

Since this is a deep-learning lab, few people have enough HPC background to compile suitable versions of numpy and PyTorch themselves, and it’s hard for them to break away from Anaconda, so the dependency on MKL is hard to remove. For this reason I needed a solution that is transparent to ordinary users.

The Solution

A widely circulated solution can be found via search engines: set the environment variable MKL_DEBUG_CPU_TYPE=5. This used to work, but it no longer works for MKL 2020 and later versions.

In the end I found a more clever solution here.

MKL calls a function mkl_serv_intel_cpu_true() to check whether it is running on an Intel CPU. As long as we provide a fake mkl_serv_intel_cpu_true() that always returns 1, we can trick MKL into thinking it is running on an Intel CPU.

To do this, we can use Linux’s LD_PRELOAD mechanism. The dynamic library pointed to by LD_PRELOAD has the highest loading priority, so as long as we compile the desired mkl_serv_intel_cpu_true() function into an so file and point LD_PRELOAD at it, we can load this function ahead of everything else.

I have often heard of the LD_PRELOAD mechanism being used for library-function hijacking attacks; here it counts as a clever use.

Implementation

Create mkl_trick.c:

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int mkl_serv_intel_cpu_true() {
    return 1;
}

Compile it with gcc -shared -fPIC -o libmkl_trick.so mkl_trick.c, and copy the generated libmkl_trick.so to /usr/local/lib.

Add the following to the shell’s global initialization file:

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export MKL_DEBUG_CPU_TYPE=5  # compatibility with older MKL versions
export MKL_ENABLE_INSTRUCTIONS=AVX2  # optional, tells MKL it can use AVX2
export LD_PRELOAD=/usr/local/lib/libmkl_trick.so

Some of my labmates use Bash and some use ZSH, so both need to be modified:

• Bash: create the file /etc/profile.d/mkl.sh and add the above content
• ZSH: add it to /etc/zsh/zshenv

References

• https://documentation.sigma2.no/jobs/mkl.html