On 11/23/10 7:19 AM, dagss 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.
It seems that I've seen it noted somewhere in previous discussions that
on non-OSX installs, ATLAS is compiled to not compute in parallel. On
OSX, IIRC, we use the optimized apple-supplied libraries that compute in
parallel.
It would be good if someone seconded what I think is our standard
configuration.
Thanks,
Jason
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