|jan 792b161753||6 months ago|
|opencl_fdfd||6 months ago|
|.gitignore||4 years ago|
|LICENSE.md||4 years ago|
|README.md||6 months ago|
|setup.py||6 months ago|
opencl_fdfd is a 3D Finite Difference Frequency Domain (FDFD) electromagnetic solver implemented in Python and OpenCL.
Currently, only periodic boundary conditions are included. PEC/PMC boundaries can be implemented by drawing PEC/PMC cells near the edges. Bloch boundary conditions are not included but wouldn’t be very hard to add.
The default solver
opencl_fdfd.cg_solver(...) located in main.py
implements the E-field wave operator directly (ie, as a list of OpenCL
instructions rather than a matrix). Additionally, there is a slower
(and slightly more versatile) solver in
csr.py which attempts to solve
an arbitrary sparse matrix in compressed sparse row (CSR) format using
the same conjugate gradient method as the default solver. The CSR solver
is significantly slower, but can be very useful for testing alternative
formulations of the FDFD electromagnetic wave equation.
Currently, this solver only uses a single GPU or other OpenCL accelerator; generalization to multiple GPUs should be pretty straightforward (ie, just copy over edge values during the matrix multiplication step).
Install with pip, via git:
pip install git+https://mpxd.net/code/jan/opencl_fdfd.git@release
See the documentation for
main.py) for details about how to call the solver.
The FDFD arguments are identical to those in
meanas.solvers.generic(...), and a few solver-specific
arguments are available.
An alternate (slower) FDFD solver and a general gpu-based sparse matrix
solver is available in
csr.py. These aren’t particularly
well-optimized, and something like
MAGMA would probably be a
better choice if you absolutely need to solve arbitrary sparse matrices
and can tolerate writing and compiling C/C++ code. Still, they’re
usually quite a bit faster than the scipy.linalg solvers.