[Numpy-discussion] Fwd: GPU Numpy
Christopher Barker
Chris.Barker@noaa....
Tue Sep 8 16:21:53 CDT 2009
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
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Christopher Barker, Ph.D.
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