matthiasnwt / fast-poisson-solver Goto Github PK

Hello, Thanks for this library; I like your approach to the problem. But I was testing and it seems to be using more GPU memory than I expected?

If I simply change the grid_num in the examples/testrun.py file to 600, precompute() runs out of memory on a 24GB GPU card [in grad() called from calculate_laplace(), or for larger matrices, within PINN.h()]. I actually need a rectangular grid of about 2300 x 800 for my problem (which corresponds in size to a square of about 1350), so it's not even close. I'm surprised by this. If I were able to precompute on a larger GPU, would solve() need less memory so I could perhaps continue to use the 24GB card?

Are there any other settings I should change to reduce the memory needed for precompute()? Would they sacrifice performance?

Also, there is a note in the README about adding support for interior boundary conditions in the future, but it seems this is already supported. Is that correct?

Many thanks