[Numpy-discussion] performance matrix multiplication vs. matlab

[David Cournapeau]

Eric Firing wrote:
>
> David,
>
> The eigen web site indicates that eigen achieves high performance 
> without all the compilation difficulty of atlas.  Does eigen have enough 
> functionality to replace atlas in numpy?

No, eigen does not provide a (complete) BLAS/LAPACK interface. I don't
know if that's even a goal of eigen (it started as a project for KDE, to
support high performance core computations for things like spreadsheet
and co).

But even then, it would be a huge undertaking. For all its flaws, LAPACK
is old, tested code, with a very stable language (F77). Eigen is:
    - not mature.
    - heavily expression-template-based C++, meaning compilation takes
ages + esoteric, impossible to decypher compilation errors. We have
enough build problems already :)
    - SSE dependency harcoded, since it is setup at build time. That's
going backward IMHO - I would rather see a numpy/scipy which can load
the optimized code at runtime.

cheers,

David