Received from Robert Kern on Fri, Nov 18, 2011 at 08:38:48AM EST:

On Fri, Nov 18, 2011 at 13:12, Neal Becker wrote:

...

Is there any possibility of incorporating this work into numpy?

http://icl.cs.utk.edu/magma/software/index.html

Into numpy itself? Very low probability, I think. The dependency on nVidia hardware and proprietary compilers makes supporting it as a build option in numpy itself too much effort for too little gain, not to mention the heuristics necessary to decide when to switch from the CPU implementations to the GPU implementations. The best value/cost ratio can probably be achieved by a separate package that wraps these routines with functions signature-compatible with those in numpy.linalg.

Apropos of the above, I've written a BSD-licensed scikit [1] that contains a variety of numpy/scipy-like functions that can take advantage of NVIDIA GPUs via PyCUDA and ctypes interfaces to several libraries provided by NVIDIA and others. I would like to add interfaces to the MAGMA library at some point, but I'm afraid I don't have time to do so in the immediate future (I'm currently adding support for some library interface changes introduced in CUDA 4.0). If anyone is interested in contributing MAGMA wrappers to the scikit, I would be happy to consider incorporating them. L.G. [1] http://github.com/lebedov/scikits.cuda