[Numpy-discussion] performance matrix multiplication vs. matlab
matthieu.brucher at gmail.com
Fri Jun 5 14:46:37 EDT 2009
2009/6/5 David Cournapeau <david at ar.media.kyoto-u.ac.jp>:
> Eric Firing wrote:
>> 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.
I would add that it relies on C++ compiler extensions (the restrict
keyword) as does blitz. You unfortunately can't expect every compiler
to support it unless the C++ committee finally adds it to the
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