Adding GPU Support

Early ideas:

1. Accept device flag for GF instances. If CUDA, use a cupy array.

2. Use cupy.get_array_module for device agnostic code where possible.

3. Pytorch-like .to(device): allow transferring between host and device(s). Internally this would just be a numpy{cupy}.asarray or Array.view(np/cp.ndarray) call.

4. Most numpy functions in galois/field/linalg.py have corresponding cupy ones with identical syntax.

5. Numba jit functions and ufuncs may require separate GPU implementations, especially if thread and block index need to be accessed.

[peter64]

hey @mhostetter thanks so much for asking, but I have no thoughts regarding a desired API. I can’t promise I will end up using the library in the end, but I am very curious to see how it will perform and will be happy to test its performance! Thanks again for writing this library and being able to add GPU support!

[peter64]

>>> print(galois.__version__)
0.0.21

Perhaps it’s because my arrays are large enough they don’t fit in the CPU cache or something…