These days, one can have 64 cores in a single node. I wonder how well the dense matrix-matrix product (SGEMM and DGEMM) scales with multiple CPUs/cores?

I tried to find some relevant benchmarks, but couldn't.


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In comparison with things like matrix vector multiplication (in which there's no cache reuse and everything has to come out of memory), matrix-matrix multiplication allows for lots of cache reuse in a careful implementation. Performance depends on having a good implementation of BLAS and perhaps depends on how much memory bandwidth is available although is much less of an issue that it was 10-20 years ago.

Over the last decade, in my own testing, I've been seeing at least 80% parallel efficiency in DGEMM for reasonably large matrices (say N=5000) on dual socket Xeon servers with up to 8 cores running well tuned BLAS implementations (ATLAS, OpenBlas, MKL, etc.) I've never had a machine with more than 8 cores that I've tested, so I won't comment further about larger numbers of processors. Don't expect good parallel efficiency for small matrices (even N=1000 is small for this.)

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    $\begingroup$ A report from the University of Maryland discusses the scaling of dgemm performance on Maryland's "Mills" cluster: docs.hpc.udel.edu/_media/technical/whitepaper/… $\endgroup$ Jul 18, 2014 at 17:26
  • $\begingroup$ Thanks for the link. I didn't even know about shared FPUs. $\endgroup$
    – MWB
    Jul 18, 2014 at 22:12
  • $\begingroup$ The shared FPU is a feature of the AMD "Bulldozer" processors. These are not processors that I would recommend for high performance scientific computing. $\endgroup$ Jul 18, 2014 at 22:33
  • $\begingroup$ The basic point here is that you should be getting nearly linear speedup on matrix multiplication. $\endgroup$ Jul 18, 2014 at 22:34
  • $\begingroup$ Why not? Xeon E5-4620 and Opteron 6378, for example, have comparable specs (cache and frequency), but the Opteron can use faster RAM, has 16 cores instead of 8 (but the same number of FPUs I believe) and costs half as much. $\endgroup$
    – MWB
    Jul 18, 2014 at 23:44

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