Standard D, Mir D benchmarks against Numpy (BLAS)

[jmh530]

On Thursday, 12 March 2020 at 12:59:41 UTC, Pavel Shkadzko wrote:
> [snip]

Looked into some of those that aren't faster than numpy:

For dot product, (what I would just call matrix multiplication), 
both functions are using gemm. There might be some quirks that 
have caused a difference in performance, but otherwise I would 
expect to be pretty close and it is. It looks like you are 
allocating the output matrix with the GC, which could be a driver 
of the difference.

For the L2-norm, you are calculating the L2 norm entry-wise as a 
Froebenius norm. That should be the same as the default for 
numpy. For numpy, the only difference I can tell between yours 
and there is that it re-uses its dot product function. Otherwise 
it looks the same.