Something looks dubious with the benchmarking here to me.
Top-tier numerical linear algebra libraries hold all hit the same number (give or take a few percent) for matrix multiply, because they're all achieving the same hardware peak performance.
Top-tier numerical linear algebra libraries hold all hit the same number (give or take a few percent) for matrix multiply, because they're all achieving the same hardware peak performance.
i plan to address that after refactoring the benches by executing each library individually
Also stops them from grumbling after you post good results!
and of course i do check the cpu utilization to make sure that all threads are spinning for multithreaded benchmarks, and occasionally check the assembly of the hot loops to make sure that the libraries were built properly and are dispatching to the right code. (avx2, avx512, etc) so overall i try to take it seriously and i'll give credit where credit is due when it turns out another library is faster