Benchmarking mir.ndslice + lubeck against numpy and Julia

[jmh530]

On Monday, 13 January 2020 at 17:09:29 UTC, Joseph Rushton 
Wakeling wrote:
> [snip]
>
> Well, see the link I posted for some details on how they 
> achieve that -- for example, when doing QR or LU decomposition, 
> instead of doing in-place calculations where they have to 
> replace every element of a m*n matrix, they define custom types 
> that store the matrix factorizations in packed representations 
> that only include the non-zero elements.

...take a look at the Julia benchmark in the first post. They are 
about 350x faster than the Numpy and D versions that are 
basically just calling C code. Do you really that the people who 
write linear algebra code are missing 350x improvements? Maybe 
their algorithm is faster than what lapack does, but I'm 
skeptical that - properly benchmarked - it could be that much 
faster.