Hameer Abbasi writes:

There's ClPy for OpenCL: https://github.com/fixstars/clpy Also this pull request for CuPy (merged, but as yet unreleased): https://github.com/cupy/cupy/pull/1094

This is great hope. Thanks for sharing this. I wonder why NVIDIA's approach is so widely accepted. Sometimes, I regret purchasing AMD GPUs. Not much support for them. -- Regards, Pankaj Jangid

Juan Nunez-Iglesias 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.

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On 19 Oct 2019, at 3:39 am, Pankaj Jangid 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