GPU Solver Enabled Via Env Variable

[SoundsSerious]

Created a PyNite Solvers module to handle importing of numpy / scipy solvers or the creation of torch based GPU solvers with the same names

Torch GPU Solver is only used when the env-var PYNITE_GPU is "True", which will alert the user that it isn't set with a "GPU not available: PYNITE_GPU enviornonmental variable not set to True"

If torch isn't installed or otherwise has a configuration error the user will get a corresponding error message "GPU not available: "

The only other thing I would add to this is a GPU field in the extra_require section of setup.py for torch

The testing suite passes in ~40s vs ~60s on a nvidia 4070 on my machine.

[JWock82]

This is an interesting thought. From what I've gathered, GPU calculations are much faster for dense matrices, but don't handle sparse matrices. I see your code converts the sparse matrices back to dense matrices prior to the GPU solution. That is a bit misleading to the user if they request a sparse solution and get a dense GPU solution instead.

Is GPU solution of a dense matrix faster than a CPU solution of a sparse matrix? I'm not sure. Most stiffness matrices are sparse (lots of zero terms except around the diagonal of the matrix). I think your code is going to be faster for small models, and possibly slower for large models.

It seems that your code is making the GPU solution the default. I think it would be better to leave the existing solvers as the default, with the GPU solver as a third option. If we play with it and feel it is faster all-around we could later change it to be the default.

I don't want to complain about code you shared freely, but it'd be nice to have more comments in the code. I'm not familiar with pytorch, so I had to use ChatGPT to help me dissect what was going on here. I appreciate you sharing this idea.