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65 lines
2.4 KiB
Markdown
65 lines
2.4 KiB
Markdown
# opencl_fdfd
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**opencl_fdfd** is a 3D Finite Difference Frequency Domain (FDFD)
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electromagnetic solver implemented in Python and OpenCL.
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**Capabilities:**
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* Arbitrary distributions of the following:
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* Dielectric constant (```epsilon```)
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* Magnetic permeabilty (```mu```)
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* Perfect electric conductor (```PEC```)
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* Perfect magnetic conductor (```PMC```)
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* Variable-sized rectangular grids
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* Stretched-coordinate PMLs (complex cell sizes allowed)
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Currently, only periodic boundary conditions are included.
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PEC/PMC boundaries can be implemented by drawing PEC/PMC cells near the edges.
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Bloch boundary conditions are not included but wouldn't be very hard to add.
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The default solver ```opencl_fdfd.cg_solver(...)``` located in main.py
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implements the E-field wave operator directly (ie, as a list of OpenCL
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instructions rather than a matrix). Additionally, there is a slower
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(and slightly more versatile) solver in ```csr.py``` which attempts to solve
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an arbitrary sparse matrix in compressed sparse row (CSR) format using
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the same conjugate gradient method as the default solver. The CSR solver
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is significantly slower, but can be very useful for testing alternative
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formulations of the FDFD electromagnetic wave equation.
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Currently, this solver only uses a single GPU or other OpenCL accelerator;
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generalization to multiple GPUs should be pretty straightforward
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(ie, just copy over edge values during the matrix multiplication step).
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## Installation
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**Dependencies:**
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* python 3 (written and tested with 3.5)
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* numpy
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* pyopencl
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* jinja2
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* [fdfd_tools](https://mpxd.net/code/jan/fdfd_tools) (>=0.2)
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Install with pip, via git:
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```bash
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pip install git+https://mpxd.net/code/jan/opencl_fdfd.git@release
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```
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## Use
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See the documentation for ```opencl_fdfd.cg_solver(...)```
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(located in ```main.py```) for details about how to call the solver.
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The FDFD arguments are identical to those in
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```fdfd_tools.solvers.generic(...)```, and a few solver-specific
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arguments are available.
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An alternate (slower) FDFD solver and a general gpu-based sparse matrix
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solver is available in ```csr.py```. These aren't particularly
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well-optimized, and something like
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[MAGMA](http://icl.cs.utk.edu/magma/index.html) would probably be a
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better choice if you absolutely need to solve arbitrary sparse matrices
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and can tolerate writing and compiling C/C++ code. Still, they're
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usually quite a bit faster than the scipy.linalg solvers.
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