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README.md
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README.md
@ -1,3 +1,37 @@
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# opencl_fdfd
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OpenCL FDFD solver
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**opencl_fdfd** is a 3D Finite Difference Frequency Domain (FDFD)
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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 implements
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the E-field wave operator directly (ie, as a list of OpenCL instructions
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rather than a matrix). Additionally, there is a slower (and slightly more
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versatile) sovler in csr.py which attempts to solve an arbitrary sparse
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matrix in compressed sparse row (CSR) format using the same conjugate gradient
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method as the default solver. The CSR solver is significantly slower, but can
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be very useful for testing alternative formulations of the FDFD 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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**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/gogs/jan/fdfd_tools)
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@ -1 +1,42 @@
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"""
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opencl_fdfd OpenCL 3D FDFD solver
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opencl_fdfd is a 3D Finite Difference Frequency Domain (FDFD) solver implemented in
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Python and OpenCL. Its capabilities include:
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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 implements
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the E-field wave operator directly (ie, as a list of OpenCL instructions
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rather than a matrix). Additionally, there is a slower (and slightly more
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versatile) sovler in csr.py which attempts to solve an arbitrary sparse
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matrix in compressed sparse row (CSR) format using the same conjugate gradient
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method as the default solver. The CSR solver is significantly slower, but can
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be very useful for testing alternative formulations of the FDFD wave equation.
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Currently, this solver only uses a single GPU or other OpenCL accelerator; generalization
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to multiple GPUs should be pretty straightforward (ie, just copy over edge values during the
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matrix multiplication step).
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Dependencies:
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- fdfd_tools ( https://mpxd.net/gogs/jan/fdfd_tools )
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- numpy
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- pyopencl
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- jinja2
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"""
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from .main import cg_solver
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__author__ = 'Jan Petykiewicz'
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@ -1,27 +1,53 @@
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from typing import List, Dict, Any
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import time
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import numpy
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from numpy.linalg import norm
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import pyopencl
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import pyopencl.array
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import time
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import fdfd_tools.operators
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from . import ops
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class CSRMatrix(object):
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"""
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Matrix stored in Compressed Sparse Row format, in GPU RAM.
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"""
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row_ptr = None # type: pyopencl.array.Array
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col_ind = None # type: pyopencl.array.Array
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data = None # type: pyopencl.array.Array
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def __init__(self, queue, m):
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def __init__(self,
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queue: pyopencl.CommandQueue,
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m: 'scipy.sparse.csr_matrix'):
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self.row_ptr = pyopencl.array.to_device(queue, m.indptr)
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self.col_ind = pyopencl.array.to_device(queue, m.indices)
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self.data = pyopencl.array.to_device(queue, m.data.astype(numpy.complex128))
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def cg(a, b, max_iters=10000, err_thresh=1e-6, context=None, queue=None, verbose=False):
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def cg(a: 'scipy.sparse.csr_matrix',
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b: numpy.ndarray,
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max_iters: int = 10000,
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err_threshold: float = 1e-6,
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context: pyopencl.Context = None,
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queue: pyopencl.CommandQueue = None,
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verbose: bool = False,
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) -> numpy.ndarray:
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"""
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General conjugate-gradient solver for sparse matrices, where A @ x = b.
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:param a: Matrix to solve (CSR format)
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:param b: Right-hand side vector (dense ndarray)
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:param max_iters: Maximum number of iterations
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:param err_threshold: Error threshold for successful solve, relative to norm(b)
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:param context: PyOpenCL context. Will be created if not given.
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:param queue: PyOpenCL command queue. Will be created if not given.
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:param verbose: Whether to print statistics to screen.
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:return: Solution vector x; returned even if solve doesn't converge.
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"""
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start_time = time.perf_counter()
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if context is None:
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@ -44,7 +70,6 @@ def cg(a, b, max_iters=10000, err_thresh=1e-6, context=None, queue=None, verbose
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m = CSRMatrix(queue, a)
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'''
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Generate OpenCL kernels
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'''
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@ -77,7 +102,7 @@ def cg(a, b, max_iters=10000, err_thresh=1e-6, context=None, queue=None, verbose
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if verbose:
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print('err', errs[-1])
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if errs[-1] < err_thresh:
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if errs[-1] < err_threshold:
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success = True
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break
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@ -108,7 +133,38 @@ def cg(a, b, max_iters=10000, err_thresh=1e-6, context=None, queue=None, verbose
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return x
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def cg_solver(omega, dxes, J, epsilon, mu=None, pec=None, pmc=None, adjoint=False, solver_opts=None):
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def cg_solver(omega: complex,
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dxes: List[List[numpy.ndarray]],
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J: numpy.ndarray,
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epsilon: numpy.ndarray,
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mu: numpy.ndarray = None,
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pec: numpy.ndarray = None,
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pmc: numpy.ndarray = None,
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adjoint: bool = False,
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solver_opts: Dict[str, Any] = None,
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) -> numpy.ndarray:
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"""
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Conjugate gradient FDFD solver using CSR sparse matrices, mainly for
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testing and development since it's much slower than the solver in main.py.
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All ndarray arguments should be 1D arrays. To linearize a list of 3 3D ndarrays,
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either use fdfd_tools.vec() or numpy:
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f_1D = numpy.hstack(tuple((fi.flatten(order='F') for fi in [f_x, f_y, f_z])))
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:param omega: Complex frequency to solve at.
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:param dxes: [[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]] (complex cell sizes)
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:param J: Electric current distribution (at E-field locations)
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:param epsilon: Dielectric constant distribution (at E-field locations)
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:param mu: Magnetic permeability distribution (at H-field locations)
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:param pec: Perfect electric conductor distribution
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(at E-field locations; non-zero value indicates PEC is present)
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:param pmc: Perfect magnetic conductor distribution
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(at H-field locations; non-zero value indicates PMC is present)
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:param adjoint: If true, solves the adjoint problem.
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:param solver_opts: Passed as kwargs to opencl_fdfd.csr.cg(**solver_opts)
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:return: E-field which solves the system.
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"""
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if solver_opts is None:
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solver_opts = dict()
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from typing import List
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import time
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import numpy
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from numpy.linalg import norm
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import pyopencl
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import pyopencl.array
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import time
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import fdfd_tools.operators
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from . import ops
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__author__ = 'Jan Petykiewicz'
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def cg_solver(omega: complex,
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dxes: List[List[numpy.ndarray]],
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J: numpy.ndarray,
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epsilon: numpy.ndarray,
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mu: numpy.ndarray = None,
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pec: numpy.ndarray = None,
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pmc: numpy.ndarray = None,
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adjoint: bool = False,
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max_iters: int = 40000,
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err_threshold: float = 1e-6,
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context: pyopencl.Context = None,
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verbose: bool = False,
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) -> numpy.ndarray:
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"""
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OpenCL FDFD solver using the iterative conjugate gradient (cg) method
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and implementing the diagonalized E-field wave operator directly in
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OpenCL.
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All ndarray arguments should be 1D arrays. To linearize a list of 3 3D ndarrays,
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either use fdfd_tools.vec() or numpy:
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f_1D = numpy.hstack(tuple((fi.flatten(order='F') for fi in [f_x, f_y, f_z])))
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:param omega: Complex frequency to solve at.
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:param dxes: [[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]] (complex cell sizes)
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:param J: Electric current distribution (at E-field locations)
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:param epsilon: Dielectric constant distribution (at E-field locations)
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:param mu: Magnetic permeability distribution (at H-field locations)
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:param pec: Perfect electric conductor distribution
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(at E-field locations; non-zero value indicates PEC is present)
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:param pmc: Perfect magnetic conductor distribution
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(at H-field locations; non-zero value indicates PMC is present)
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:param adjoint: If true, solves the adjoint problem.
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:param max_iters: Maximum number of iterations. Default 40,000.
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:param err_threshold: If (r @ r.conj()) / norm(1j * omega * J) < err_threshold, success.
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Default 1e-6.
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:param context: PyOpenCL context to run in. If not given, construct a new context.
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:param verbose: If True, print progress to stdout. Default False.
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:return: E-field which solves the system. Returned even if we did not converge.
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"""
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def cg_solver(omega, dxes, J, epsilon, mu=None, pec=None, pmc=None, adjoint=False,
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max_iters=40000, err_thresh=1e-6, context=None, verbose=False):
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start_time = time.perf_counter()
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b = -1j * omega * J
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@ -138,7 +179,7 @@ def cg_solver(omega, dxes, J, epsilon, mu=None, pec=None, pmc=None, adjoint=Fals
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if verbose:
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print('err', errs[-1])
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if errs[-1] < err_thresh:
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if errs[-1] < err_threshold:
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success = True
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break
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from typing import List, Callable
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import numpy
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import jinja2
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@ -9,6 +11,9 @@ from pyopencl.reduction import ReductionKernel
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# Create jinja2 env on module load
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jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__, 'kernels'))
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# Return type for the create_opname(...) functions
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operation = Callable[..., List[pyopencl.Event]]
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def type_to_C(float_type: numpy.float32 or numpy.float64) -> str:
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"""
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@ -28,9 +33,11 @@ def type_to_C(float_type: numpy.float32 or numpy.float64) -> str:
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return types[float_type]
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# Type names
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ctype = type_to_C(numpy.complex128)
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ctype_bare = 'cdouble'
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# Preamble for all OpenCL code
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preamble = '''
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#define PYOPENCL_DEFINE_CDOUBLE
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#include <pyopencl-complex.h>
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'''.format(ctype=ctype_bare)
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def ptrs(*args):
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def ptrs(*args: str) -> List[str]:
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return [ctype + ' *' + s for s in args]
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def create_a(context, shape, mu=False, pec=False, pmc=False):
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def create_a(context: pyopencl.Context,
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shape: numpy.ndarray,
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mu: bool = False,
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pec: bool = False,
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pmc: bool = False,
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) -> operation:
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"""
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Return a function which performs (A @ p), where A is the FDFD wave equation for E-field.
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The returned function has the signature
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spmv(E, H, p, idxes, oeps, inv_mu, pec, pmc, Pl, Pr, e)
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with arguments (all except e are of type pyopencl.array.Array (or contain it)):
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E E-field (output)
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H Temporary variable for holding intermediate H-field values on GPU (same size as E)
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p p-vector (input vector)
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idxes list holding [[1/dx_e, 1/dy_e, 1/dz_e], [1/dx_h, 1/dy_h, 1/dz_h]] (complex cell widths)
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oeps omega * epsilon
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inv_mu 1/mu
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pec array of bytes; nonzero value indicates presence of PEC
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pmc array of bytes; nonzero value indicates presence of PMC
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Pl Left preconditioner (array containing diagonal entries only)
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Pr Right preconditioner (array containing diagonal entries only)
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e List of pyopencl.Event; execution will wait until these are finished.
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and returns a list of pyopencl.Event.
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:param context: PyOpenCL context
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:param shape: Dimensions of the E-field
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:param mu: False iff (mu == 1) everywhere
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:param pec: False iff no PEC anywhere
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:param pmc: False iff no PMC anywhere
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:return: Function for computing (A @ p)
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"""
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common_source = jinja_env.get_template('common.cl').render(shape=shape)
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des = [ctype + ' *inv_de' + a for a in 'xyz']
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dhs = [ctype + ' *inv_dh' + a for a in 'xyz']
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'''
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Convert p to initial E (ie, apply right preconditioner and PEC)
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'''
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p2e_source = jinja_env.get_template('p2e.cl').render(pec=pec)
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P2E_kernel = ElementwiseKernel(context,
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name='P2E',
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operation=p2e_source,
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arguments=', '.join(ptrs('E', 'p', 'Pr') + pec_arg))
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'''
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Calculate intermediate H from intermediate E
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'''
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e2h_source = jinja_env.get_template('e2h.cl').render(mu=mu,
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pmc=pmc,
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common_cl=common_source)
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@ -73,6 +118,9 @@ def create_a(context, shape, mu=False, pec=False, pmc=False):
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operation=e2h_source,
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arguments=', '.join(ptrs('E', 'H', 'inv_mu') + pmc_arg + des))
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'''
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Calculate final E (including left preconditioner)
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'''
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h2e_source = jinja_env.get_template('h2e.cl').render(pec=pec,
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common_cl=common_source)
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H2E_kernel = ElementwiseKernel(context,
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@ -90,7 +138,20 @@ def create_a(context, shape, mu=False, pec=False, pmc=False):
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return spmv
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def create_xr_step(context):
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def create_xr_step(context: pyopencl.Context) -> operation:
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"""
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Return a function
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xr_update(x, p, r, v, alpha, e)
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which performs the operations
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x += alpha * p
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r -= alpha * v
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after waiting for all in the list e
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and returns a list of pyopencl.Event
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:param context: PyOpenCL context
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:return: Function for performing x and r updates
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"""
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update_xr_source = '''
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x[i] = add(x[i], mul(alpha, p[i]));
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r[i] = sub(r[i], mul(alpha, v[i]));
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@ -110,13 +171,28 @@ def create_xr_step(context):
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return xr_update
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def create_rhoerr_step(context):
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def create_rhoerr_step(context: pyopencl.Context) -> operation:
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"""
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Return a function
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ri_update(r, e)
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which performs the operations
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rho = r * r.conj()
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err = r * r
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after waiting for all pyopencl.Event in the list e
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and returns a list of pyopencl.Event
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:param context: PyOpenCL context
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:return: Function for performing x and r updates
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"""
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update_ri_source = '''
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(double3)(r[i].real * r[i].real, \
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r[i].real * r[i].imag, \
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r[i].imag * r[i].imag)
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'''
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# Use a vector type (double3) to make the reduction simpler
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ri_dtype = pyopencl.array.vec.double3
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ri_kernel = ReductionKernel(context,
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@ -138,7 +214,19 @@ def create_rhoerr_step(context):
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return ri_update
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def create_p_step(context):
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def create_p_step(context: pyopencl.Context) -> operation:
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"""
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Return a function
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p_update(p, r, beta, e)
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which performs the operation
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p = r + beta * p
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after waiting for all pyopencl.Event in the list e
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and returns a list of pyopencl.Event
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:param context: PyOpenCL context
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:return: Function for performing the p update
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"""
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update_p_source = '''
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p[i] = add(r[i], mul(beta, p[i]));
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'''
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@ -156,7 +244,16 @@ def create_p_step(context):
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return p_update
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def create_dot(context):
|
||||
def create_dot(context: pyopencl.Context) -> operation:
|
||||
"""
|
||||
Return a function for performing the dot product
|
||||
p @ v
|
||||
with the signature
|
||||
dot(p, v, e) -> float
|
||||
|
||||
:param context: PyOpenCL context
|
||||
:return: Function for performing the dot product
|
||||
"""
|
||||
dot_dtype = numpy.complex128
|
||||
|
||||
dot_kernel = ReductionKernel(context,
|
||||
@ -168,14 +265,30 @@ def create_dot(context):
|
||||
reduce_expr='add(a, b)',
|
||||
arguments=ptrs('p', 'v'))
|
||||
|
||||
def ri_update(p, v, e):
|
||||
def dot(p, v, e):
|
||||
g = dot_kernel(p, v, wait_for=e)
|
||||
return g.get()
|
||||
|
||||
return ri_update
|
||||
return dot
|
||||
|
||||
|
||||
def create_a_csr(context):
|
||||
def create_a_csr(context: pyopencl.Context) -> operation:
|
||||
"""
|
||||
Return a function for performing the operation
|
||||
(N @ v)
|
||||
where N is stored in CSR (compressed sparse row) format.
|
||||
|
||||
The function signature is
|
||||
spmv(v_out, m, v_in, e)
|
||||
where m is an opencl_fdfd.csr.CSRMatrix
|
||||
and v_out, v_in are (dense) vectors (of type pyopencl.array.Array).
|
||||
|
||||
The function waits on all the pyopencl.Event in e before running, and returns
|
||||
a list of pyopencl.Event.
|
||||
|
||||
:param context: PyOpenCL context
|
||||
:return: Function for sparse (M @ v) operation where M is in CSR format
|
||||
"""
|
||||
spmv_source = '''
|
||||
int start = m_row_ptr[i];
|
||||
int stop = m_row_ptr[i+1];
|
||||
|
||||
Loading…
Reference in New Issue
Block a user