Hello, I decided to make couple of benchmarks against NumPy out of curiosity.
Since I don’t know Julia it would be nice if someone could take a second look because based on what I measured it doesn’t look like Julia is faster especially in matrix multiplication and norm.

It matters a lot how these benchmarks are executed. Numpy uses a blas library with a certain number of threads, executing for instance matrix-matrix multiplication in parallel. If one does not start Julia with multiple threads, it is hobbled.

There are several threads on this forum where you will find more information. Search for numpy and benchmark.

And anyway, as you seem to know, all the work is done by the library. If you run both with the same library, then you will be benchmarking your benchmarking technique…

FWIW, on my computer the difference seems much less than 10x for test4, is it really this different on your computer, or was there a copy-paste error?

julia> @btime $float_matrixA * $float_matrixC; # test4, MKL — 0.001_856 in your table
946.013 μs (2 allocations: 1.91 MiB)
julia> @btime $float_matrixA * $float_matrixC; # test4, OpenBLAS — 0.019_880 in your table
1.191 ms (2 allocations: 1.91 MiB)