[microtonal]

The plot thickens. As I reported elsewhere in the thread, the slow code paths were selected on my machine, unless I override the mkl_serv_intel_cpu_true function to always return true. However, this was with PyTorch.

I have now also compiled the ACE DGEMM benchmark and linked against MKL iomp:

    $ ./mt-dgemm 1000 | grep GFLOP
    GFLOP/s rate:         69.124168 GF/s

Most-used function is

   mt-dgemm  libmkl_def.so       [.] mkl_blas_def_dgemm_kernel_zen

So, it is clearly using a GEMM kernel. Now I wonder what is different between PyTorch and this simple benchmark, causing PyTorch to result in a slow SSE code path.

[microtonal]

Found the discrepancy. I use single precision in PyTorch. When I benchmark sgemm, the SSE code path is selected.

Conclusion: MKL detects Zen now, but currently only implements a Zen code path for dgemm and not for sgemm. To get good performance for sgemm, you have to fake being an Intel CPU.

Edit, longer description: https://github.com/pytorch/builder/issues/504