George Dahl wrote: > Sturla Molden <sturla <at> molden.no> writes: >> Teraflops peak performance of modern GPUs is impressive. But NumPy >> cannot easily benefit from that.
> I know that for my work, I can get around an order of a 50-fold speedup over > numpy using a python wrapper for a simple GPU matrix class. I think you're talking across each other here. Sturla is referring to making a numpy ndarray gpu-aware and then expecting expressions like: z = a*x**2 + b*x + c to go faster when s, b, c, and x are ndarrays. That's not going to happen. On the other hand, George is talking about moving higher-level operations (like a matrix product) over to GPU code. This is analogous to numpy.linalg and numpy.dot() using LAPACK routines, and yes, that could help those programs that use such operations. So a GPU LAPACK would be nice. This is also analogous to using SWIG, or ctypes or cython or weave, or ??? to move a computationally expensive part of the code over to C. I think anything that makes it easier to write little bits of your code for the GPU would be pretty cool -- a GPU-aware Cython? Also, perhaps a GPU-aware numexpr could be helpful which I think is the kind of thing that Sturla was refering to when she wrote: "Incidentally, this will also make it easier to leverage on modern GPUs." -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
