Konrad Hinsen wrote:
NumPy uses only one processor, and I am not even sure I'd want to change that. I use biprocessor machines as well and I have adapted my time-critical code to them (parallelization via threading), but the parallelization is almost always at a higher level than NumPy operations. In other words, I give one NumPy operation to each processor rather than have both work on the same NumPy operation.
I do the same thing. And I agree about not wanting it the other way (although an option for this might be nice...)
I'd prefer to build a parallelizing general-purpose library on top of NumPy, ideally supporting message passing as well. Would anyone else be interested in this? I have a nicely packaged MPI support module for Python (to be released next week in a new version of ScientificPython), so that part is already done.
I am certainly interested. In fact, I have also written a MPI support module. Maybe when I see yours I will be able to add some stuff...I'm making the assumption that yours is probably more flexible than mine...
Which reminds me: there once was a parallelization project mailing list on the old Starship, which disappeared during the move due to a minor accident. Is there interest to revive it? I now have a cluster of 20 biprocessors to feed, and I'd like to provide it with only the best: Python code ;-)
Once again, I'm in... Scott -- Scott M. Ransom Address: Harvard-Smithsonian CfA Phone: (617) 495-4142 60 Garden St. MS 10 email: ransom@cfa.harvard.edu Cambridge, MA 02138 PGP Fingerprint: D2 0E D0 10 CD 95 06 DA EF 78 FE 2B CB 3A D3 53