Hi Doug, I've seen pretty decent performance on the AMD processors, and was even told by AMD people to use the Intel compiler -- but when doing that, we specify the processor type (eg, AVX capabilities), and it works pretty well. However, I don't have any experience using the MKL on them.
That said, looking at the numbers, it's pretty interesting that there's roughly a factor of 2 from the AVX2 (OpenBLAS) -> AVX512 (MKL) results on Intel, and with the two systems being relatively comparable with OpenBLAS (AVX2). Then it's *roughly* a factor of 8 going from the MKL on Intel to the MKL on AMD, and since AVX512 is 8 x 64 floats, it seems it could just be it's not using any vectorization whatsoever on AMD... presumably because Intel claims they can't recognize the chip? That said, I'd love to see the author try after setting: MKL_ENABLE_INSTRUCTIONS=AVX2 That might be an easy fix, if it works[1]. Anyone got a Zen2 system with NumPy & the MKL to try it with? - Brian [1] https://software.intel.com/en-us/mkl-linux-developer-guide-instruction-set-specific-dispatching-on-intel-architectures On Thu, Dec 12, 2019 at 7:35 AM Douglas Eadline <deadl...@eadline.org> wrote: > > Anyone see anything like this with Epyc, i.e. poor AMD performance > when using Intel compilers or MKL? > > > https://www.pugetsystems.com/labs/hpc/AMD-Ryzen-3900X-vs-Intel-Xeon-2175W-Python-numpy---MKL-vs-OpenBLAS-1560/ > > > > -- > Doug > > _______________________________________________ > Beowulf mailing list, Beowulf@beowulf.org sponsored by Penguin Computing > To change your subscription (digest mode or unsubscribe) visit > https://beowulf.org/cgi-bin/mailman/listinfo/beowulf >
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