[Numpy-discussion] performance matrix multiplication vs. matlab
Fri Jun 5 16:37:49 CDT 2009
I'll caution anyone from using Atlas from the repos in Ubuntu 9.04 as the
package is broken:
just build Atlas yourself, you get better performance AND threading.
Building it is not the nightmare it sounds like. I think i've done it a
total of four times now, both 32-bit and 64-bit builds.
If you need help with it, just email me off list.
On Fri, Jun 5, 2009 at 2:46 PM, Matthieu Brucher <firstname.lastname@example.org
> 2009/6/5 David Cournapeau <email@example.com>:
> > 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.
> 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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