opencl_fdfd/README.md

# opencl_fdfd

**opencl_fdfd** is a 3D Finite Difference Frequency Domain (FDFD)
solver implemented in Python and OpenCL.
  
**Capabilities**
* Arbitrary distributions of the following:
    * Dielectric constant (epsilon)
    * Magnetic permeabilty (mu)
    * Perfect electric conductor (PEC)
    * Perfect magnetic conductor (PMC)
* Variable-sized rectangular grids
    * Stretched-coordinate PMLs (complex cell sizes allowed)

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 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).


## Installation

**Dependencies:**
* python 3 (written and tested with 3.5) 
* numpy
* pyopencl
* jinja2
* [fdfd_tools](https://mpxd.net/gogs/jan/fdfd_tools)


Install with pip, via git:
```bash
pip install git+https://mpxd.net/gogs/jan/opencl_fdfd.git@release
```


## Use

See the documentation for opencl_fdfd.cg_solver(...)
(located in main.py) for details about how to call the solver.
 
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](http://icl.cs.utk.edu/magma/index.html) would
probably be a better choice if you absolutely need to solve arbitrary
sparse matrices and can tolerate writing and compiling C/C++ code.