Martin Ünsal <martinunsal <at> gmail.com> writes: > > I was wondering if anyone has thought about accelerating NumPy with a > GPU. For example nVidia's CUDA SDK provides a feasible way to offload > vector math onto the very fast SIMD processors available on the GPU. > Currently GPUs primarily support single precision floats and are not > IEEE compliant, but still could be useful for some applications. > > If there turns out to be a significant speedup over using the CPU, this > could be a very accessible way to do scientific and numerical > computation using GPUs, much easier than coding directly to the GPU APIs. > > Martin >
I've thought about this too and think that it's a great idea. The existing library Brook, which has a similar programming model to NumPy, proves that it's feasible. And Brook was originally done with OpenGL & DirectX as backends to access the hardware. Needless to say, that's a lot harder than using CUDA. Since it hasn't already been pointed out, CUDA includes the cuBLAS and cuFFT libraries. I don't what the status of a LAPACK built on top of the cuBLAS is, but I'd be surprised if someone isn't already working on it. Also, NVIDIA has stated that double-precision hardware will be available later this year, in case that's an issue for anyone. I agree very much that it would make the GPUs more accessible, although CUDA has done an amazing job at that already. I think the most helpful thing about this would be if it allowed us to code using the existing array interface from NumPy in a way that the code automatically runs on the GPU in an optimized way - using shared memory + avoiding bank conflicts. I'd happily contribute to such a project if someone else got it started. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
