Juan Nunez-Iglesias <j...@fastmail.com> writes: > I have also used PyOpenCL quite profitably: > > https://github.com/inducer/pyopencl <https://github.com/inducer/pyopencl> > > I philosophically prefer it to ROCm because it targets *all* GPUs, including > intel integrated graphics on most laptops, which can actually get quite > decent (30x) speedups. >
This is a good find. There is some work involved but it is good. It gives transparent access to underlying hardware. I wish NumPy operations automatically use the available resources. That is more concise. It will give scientific community an edge. I am not saying they are not good programmers but still it will let them focus on the main problem at hand. Let me explore it further. Thanks for sharing. >> On 19 Oct 2019, at 3:39 am, Pankaj Jangid <pankaj.jan...@gmail.com> wrote: >> I wonder why NVIDIA's approach is so widely accepted. Sometimes, I >> regret purchasing AMD GPUs. Not much support for them. > > I agree. I am very disappointed by the NVIDIA monopoly in scientific > computing. Resist! > Really, very disappointing. :-( Regards, -- Pankaj Jangid _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
