I wonder why zgesv comes here from lapack_lite rather than from Atlas,
which might have much faster zgesv.
Is it a feature (or a bug) of Sage configuration of Numpy?
Or in fact it does come come Atlas?

Dima

On Nov 23, 9:19 pm, dagss <da...@student.matnat.uio.no> wrote:
> On Nov 23, 3:54 am, Jason Grout <jason-s...@creativetrax.com> wrote:
>
> > On 11/22/10 1:48 PM, Ethan Van Andel wrote:
>
> > > In my development, I'm attempting to parallelize some code. However,
> > > the bottleneck is a call to numpy.linalg.lapack_lite.zgesv, that is
> > > the point where numpy calls LAPACK to solve my complex system of
> > > linear equations. Ideally I'd like to parallelize this, and I know the
> > > ScaLAPACK has a parallel version, pzgesv. Is ScaLAPACK supported by
> > > Sage, or are there any plans to support it in the future? If not, I'll
> > > have to look for workarounds.
>
> > I would definitely start by asking on the numpy list about this.  It
> > sounds interesting.
>
> AFAIK there's no existing, direct ScaLAPACK wrapper for Python.
> Perhaps I need to do something about this in spring for my research
> (in particular if I can get somebody to join me).
>
> There might be something you could use in petsc4py.
>
> NOTE that ScaLAPACK is a very different beast from LAPACK and not
> something you can drop in to magically get parallelization. ScaLAPACK
> is meant for use on an MPI cluster. You need to manually write
> parallel code.
>
> If you can do with a single node, a much easier approach is to use a
> parallel version of BLAS/LAPACK (commercial LAPACK's support this, not
> sure what the current status of ATLAS is). You can compile NumPy
> against a commercial LAPACK and have all numerical/double precision
> floating point linear algebra just run faster without any extra
> effort. E.g., the Enthought Python Distribution does this for a six-
> fold speed increase upon ATLAS.
>
> Dag Sverre

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