Why matlab benchmark extreamly slow on AMD EPYC CPU

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Hello, I have a Rack server with AMD EPYC 75F2 (16core 3.5GHz) CPU + 512G 3200MHz ECCRAM + P620 GPU +windows server 2019, build for CPU based finiate elements simulation. After I recieved the mechine, I tried t = bench (on MATLAB 2020b) to see the performance of it, but result it really bad, it can't even fight $100 Ryzen 1700, but hardware check gives no hardware error, other softawre such as COMSOL also behaves bad, can someone help me with this? Thank you!
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Chengkuan Gao
Chengkuan Gao on 3 Jan 2021
Yeah I think that's possible, it may be fixed on AMD Ryzen, but still have some issue on EPYC, hope Mathwork or AMD can fix this problem...

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Answers (3)

Kevin
Kevin on 22 Jul 2021
Hopefully mathworks will actually fix this problem. It seems to be specifically localized to Linux based Epyc systems. If you utilize a Windows OS for your Epyc system on R2021A Update 3 or R2021B you will get the expected levels of performance out of your system.
It appears right now though, at least on CentOS 7.8 installs, Epyc CPUs have improperly defined vectorization characteristics. A given vectorized operation e.g.:
A = magic(20000);
[L,U,P] = lu(A);
May take 10x longer on the exact same AMD Epyc CPU on Linux compared to the same CPU running in Windows. Other functions with notable problems include isonormals, gradient, and bwareaopen to name a few. It appears to only run on a single thread in those sections of code rather than properly multithreading vectorized code operations.
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Kevin
Kevin on 21 Nov 2021
My sense is there’s no issue with the CPU at all; it’s purely down to the kernel/distro selection. I haven’t even been able to disprove whether this bug exists for Intel processors when similar memory utilization conditions exist. I would say just think about what sort of memory load you put on the system, and test how your MATLAB workloads coexist under different distros. My guess is if you use a bleeding edge kernel you’ll probably be better.

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gophi7
gophi7 on 3 Jan 2021
Edited: gophi7 on 3 Jan 2021
I am seeing the same issue with a rack EPYC cpu, and modifying maxNumCompThreads did not seem to help. Did you happen to learn what the issue is / if there is a workaround?
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Robert
Robert on 9 Apr 2021
With the original poor performance for Ryzen, it was possible to set an environment variable that had the effect of forcing the Intel MKL to use AVX2, greatly improving performance. Have you tried if this also works for EPYC? Procedure is described half-way down this post.

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