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