On Fri, Oct 6, 2017 at 7:05 AM, <josef.pktd@gmail.com> wrote:


On Thu, Oct 5, 2017 at 1:54 PM, Ralf Gommers <ralf.gommers@gmail.com> wrote:


On Thu, Oct 5, 2017 at 4:17 AM, marc <marc.barbry@mailoo.org> wrote:
I guess this problem of dependency could be avoid with a custom installation.
MPI code could be compile only if specify, in a similar fashion than the mkl library for Blas/Lapack wrappers.

There's a major difference there: MKL is optional, but BLAS/LAPACK is not. We require a BLAS/LAPACK install, the implementations can be switched out.

For ScaLAPACK it seems pretty clear we can't require it to be installed. Moreover, distributed computing is out of scope for SciPy. So developing your ScaLAPACK wrappers (which do seem very useful indeed) as a separate package looks like the better way to go.


But does distributed computing stay out of scope for SciPy after 1.0?
As a long term plan towards 2.0?

Such changes are worth discussing once in a while, usually sharpens the focus:)

My first thoughts:
- traditional stuff like MPI, BLACS, ScaLAPACK will likely always remain out of scope
- we can consider new dependencies, but only if they do not make it harder to install SciPy
- a few more likely changes would be to start allowing/supporting pandas data frames as inputs, broader use of simple (optional) parallelization with joblib or threading, and using dask under the hood.

Ralf



(Not that MPI based solutions will help much on Windows, AFAICS.)

Josef
 

Cheers,
Ralf



On 10/04/2017 04:59 PM, Joshua Wilson via gpaw-users wrote:
Just off the cuff I'd guess that the BLACS dependency would make it
very difficult to support Scalapack in SciPy proper.

On Wed, Oct 4, 2017 at 8:45 AM, marc <marc.barbry@mailoo.org> wrote:
Dear Scipy developers,

We are actually writing a python wrapper for Scalapack that seems to be
cruelly missing for computational science with Python.
Since Scipy has already wrappers for Blas and Lapack library we are hoping
that you could be interested as well in a Scalapack wrapper.
In this aim, we used f2py to wrap the routine as it is done in the
Blas/Lapack wrapper of Scipy.

The actual code can be found in the following repository,
https://gitlab.com/mbarbry/python-scalapack

Since the project just started, only few routines are implemented, but we
manage to write a working test with sygv routine.
We would like to know if the Scipy community would be interested to merge
this wrapper in the Scipy code in the future?

We believe that Scalapack is an important part of the Scientific tools for
linear algebra, and that Scipy would only benefit of such wrapper as well
than the Scientific community.
Contributions to the code are obviously more than welcome.

Best regards,
Marc Barbry
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