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3
.gitignore
vendored
3
.gitignore
vendored
@ -60,3 +60,6 @@ target/
|
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|
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# PyCharm
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.idea/
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|
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.mypy_cache/
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.pytest_cache/
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|
24
README.md
24
README.md
@ -6,10 +6,10 @@ 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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* 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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@ -17,10 +17,10 @@ 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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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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(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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@ -34,11 +34,11 @@ generalization to multiple GPUs should be pretty straightforward
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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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* python 3 (written and tested with 3.7)
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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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* [meanas](https://mpxd.net/code/jan/meanas) (>=0.5)
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Install with pip, via git:
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@ -49,14 +49,14 @@ pip install git+https://mpxd.net/code/jan/opencl_fdfd.git@release
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## Use
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See the documentation for ```opencl_fdfd.cg_solver(...)```
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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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`meanas.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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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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|
1
opencl_fdfd/LICENSE.md
Symbolic link
1
opencl_fdfd/LICENSE.md
Symbolic link
@ -0,0 +1 @@
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../LICENSE.md
|
1
opencl_fdfd/README.md
Symbolic link
1
opencl_fdfd/README.md
Symbolic link
@ -0,0 +1 @@
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../README.md
|
@ -31,14 +31,14 @@
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||||
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Dependencies:
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- fdfd_tools ( https://mpxd.net/code/jan/fdfd_tools )
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- meanas ( https://mpxd.net/code/jan/meanas )
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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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from .main import cg_solver as cg_solver
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__author__ = 'Jan Petykiewicz'
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version = '0.3'
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__version__ = '0.4'
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version = __version__
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|
@ -6,7 +6,7 @@ CSRMatrix sparse matrix representation.
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The FDFD solver (fdfd_cg_solver()) solves an FDFD problem by
|
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creating a sparse matrix representing the problem (using
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fdfd_tools) and then passing it to cg(), which performs a
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meanas) and then passing it to cg(), which performs a
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conjugate gradient solve.
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cg() is capable of solving arbitrary sparse matrices which
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@ -14,58 +14,66 @@ satisfy the constraints for the 'conjugate gradient' algorithm
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(positive definite, symmetric) and some that don't.
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"""
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from typing import Dict, Any
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from typing import Any, TYPE_CHECKING
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import time
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import logging
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import numpy
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from numpy.typing import NDArray, ArrayLike
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from numpy.linalg import norm
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from numpy import complexfloating
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import pyopencl
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import pyopencl.array
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import fdfd_tools.solvers
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import meanas.fdfd.solvers
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from . import ops
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if TYPE_CHECKING:
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import scipy
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__author__ = 'Jan Petykiewicz'
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logger = logging.getLogger(__name__)
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class CSRMatrix(object):
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class CSRMatrix:
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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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row_ptr: pyopencl.array.Array
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col_ind: pyopencl.array.Array
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data: pyopencl.array.Array
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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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def __init__(
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self,
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queue: pyopencl.CommandQueue,
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m: 'scipy.sparse.csr_matrix',
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) -> None:
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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: '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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) -> numpy.ndarray:
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def cg(
|
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A: 'scipy.sparse.csr_matrix',
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b: ArrayLike,
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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 = None,
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queue: pyopencl.CommandQueue | None = None,
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) -> NDArray[complexfloating]:
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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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:return: Solution vector x; returned even if solve doesn't converge.
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Args:
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A: Matrix to solve (CSR format)
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b: Right-hand side vector (dense ndarray)
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max_iters: Maximum number of iterations
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err_threshold: Error threshold for successful solve, relative to norm(b)
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context: PyOpenCL context. Will be created if not given.
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queue: PyOpenCL command queue. Will be created if not given.
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Returns:
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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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@ -76,10 +84,10 @@ def cg(A: 'scipy.sparse.csr_matrix',
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if queue is None:
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queue = pyopencl.CommandQueue(context)
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def load_field(v, dtype=numpy.complex128):
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def load_field(v: NDArray[numpy.complexfloating], dtype: type = numpy.complex128) -> pyopencl.array.Array:
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return pyopencl.array.to_device(queue, v.astype(dtype))
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r = load_field(b)
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r = load_field(numpy.asarray(b))
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x = pyopencl.array.zeros_like(r)
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v = pyopencl.array.zeros_like(r)
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p = pyopencl.array.zeros_like(r)
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@ -90,27 +98,27 @@ def cg(A: 'scipy.sparse.csr_matrix',
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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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#
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# Generate OpenCL kernels
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#
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a_step = ops.create_a_csr(context)
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xr_step = ops.create_xr_step(context)
|
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rhoerr_step = ops.create_rhoerr_step(context)
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p_step = ops.create_p_step(context)
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dot = ops.create_dot(context)
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|
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'''
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Start the solve
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||||
'''
|
||||
#
|
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# Start the solve
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||||
#
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||||
start_time2 = time.perf_counter()
|
||||
|
||||
_, err2 = rhoerr_step(r, [])
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b_norm = numpy.sqrt(err2)
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logging.debug('b_norm check: ', b_norm)
|
||||
logging.debug(f'b_norm check: {b_norm}')
|
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|
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success = False
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for k in range(max_iters):
|
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logging.debug('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha))
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logging.debug(f'[{k:06d}] rho {rho:.4} alpha {alpha:4.4}')
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|
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rho_prev = rho
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e = xr_step(x, p, r, v, alpha, [])
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@ -118,7 +126,7 @@ def cg(A: 'scipy.sparse.csr_matrix',
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|
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errs += [numpy.sqrt(err2) / b_norm]
|
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|
||||
logging.debug('err {}'.format(errs[-1]))
|
||||
logging.debug(f'err {errs[-1]}')
|
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|
||||
if errs[-1] < err_threshold:
|
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success = True
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@ -128,12 +136,12 @@ def cg(A: 'scipy.sparse.csr_matrix',
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e = a_step(v, m, p, e)
|
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alpha = rho / dot(p, v, e)
|
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|
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if verbose and k % 1000 == 0:
|
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logging.info('iteration {}'.format(k))
|
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if k % 1000 == 0:
|
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logger.info(f'iteration {k}')
|
||||
|
||||
'''
|
||||
Done solving
|
||||
'''
|
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#
|
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# Done solving
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||||
#
|
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time_elapsed = time.perf_counter() - start_time
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|
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x = x.get()
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@ -142,38 +150,46 @@ def cg(A: 'scipy.sparse.csr_matrix',
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logging.info('Solve success')
|
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else:
|
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logging.warning('Solve failure')
|
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logging.info('{} iterations in {} sec: {} iterations/sec \
|
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'.format(k, time_elapsed, k / time_elapsed))
|
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logging.debug('final error {}'.format(errs[-1]))
|
||||
logging.debug('overhead {} sec'.format(start_time2 - start_time))
|
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logging.info(f'{k} iterations in {time_elapsed} sec: {k / time_elapsed} iterations/sec')
|
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logging.debug(f'final error {errs[-1]}')
|
||||
logging.debug(f'overhead {start_time2 - start_time} sec')
|
||||
|
||||
logging.info('Final residual: {}'.format(norm(A @ x - b) / norm(b)))
|
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residual = norm(A @ x - b) / norm(b)
|
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logging.info(f'Final residual: {residual}')
|
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return x
|
||||
|
||||
|
||||
def fdfd_cg_solver(solver_opts: Dict[str, Any] = None,
|
||||
**fdfd_args
|
||||
) -> numpy.ndarray:
|
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def fdfd_cg_solver(
|
||||
solver_opts: dict[str, Any] | None = None,
|
||||
**fdfd_args,
|
||||
) -> NDArray[complexfloating]:
|
||||
"""
|
||||
Conjugate gradient FDFD solver using CSR sparse matrices, mainly for
|
||||
testing and development since it's much slower than the solver in main.py.
|
||||
|
||||
Calls fdfd_tools.solvers.generic(**fdfd_args,
|
||||
matrix_solver=opencl_fdfd.csr.cg,
|
||||
matrix_solver_opts=solver_opts)
|
||||
Calls meanas.fdfd.solvers.generic(
|
||||
**fdfd_args,
|
||||
matrix_solver=opencl_fdfd.csr.cg,
|
||||
matrix_solver_opts=solver_opts,
|
||||
)
|
||||
|
||||
:param solver_opts: Passed as matrix_solver_opts to fdfd_tools.solver.generic(...).
|
||||
Default {}.
|
||||
:param fdfd_args: Passed as **fdfd_args to fdfd_tools.solver.generic(...).
|
||||
Should include all of the arguments **except** matrix_solver and matrix_solver_opts
|
||||
:return: E-field which solves the system.
|
||||
Args:
|
||||
solver_opts: Passed as matrix_solver_opts to fdfd_tools.solver.generic(...).
|
||||
Default {}.
|
||||
fdfd_args: Passed as **fdfd_args to fdfd_tools.solver.generic(...).
|
||||
Should include all of the arguments **except** matrix_solver and matrix_solver_opts
|
||||
|
||||
Returns:
|
||||
E-field which solves the system.
|
||||
"""
|
||||
|
||||
if solver_opts is None:
|
||||
solver_opts = dict()
|
||||
|
||||
x = fdfd_tools.solvers.generic(matrix_solver=cg,
|
||||
matrix_solver_opts=solver_opts,
|
||||
**fdfd_args)
|
||||
x = meanas.fdfd.solvers.generic(
|
||||
matrix_solver=cg,
|
||||
matrix_solver_opts=solver_opts,
|
||||
**fdfd_args,
|
||||
)
|
||||
|
||||
return x
|
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|
@ -5,69 +5,70 @@ This file holds the default FDFD solver, which uses an E-field wave
|
||||
operator implemented directly as OpenCL arithmetic (rather than as
|
||||
a matrix).
|
||||
"""
|
||||
|
||||
from typing import List
|
||||
import time
|
||||
import logging
|
||||
|
||||
import numpy
|
||||
from numpy.typing import NDArray, ArrayLike
|
||||
from numpy.linalg import norm
|
||||
from numpy import floating, complexfloating
|
||||
import pyopencl
|
||||
import pyopencl.array
|
||||
|
||||
import fdfd_tools.operators
|
||||
import meanas.fdfd.operators
|
||||
|
||||
from . import ops
|
||||
|
||||
|
||||
__author__ = 'Jan Petykiewicz'
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def cg_solver(omega: complex,
|
||||
dxes: List[List[numpy.ndarray]],
|
||||
J: numpy.ndarray,
|
||||
epsilon: numpy.ndarray,
|
||||
mu: numpy.ndarray = None,
|
||||
pec: numpy.ndarray = None,
|
||||
pmc: numpy.ndarray = None,
|
||||
adjoint: bool = False,
|
||||
max_iters: int = 40000,
|
||||
err_threshold: float = 1e-6,
|
||||
context: pyopencl.Context = None,
|
||||
) -> numpy.ndarray:
|
||||
def cg_solver(
|
||||
omega: complex,
|
||||
dxes: list[list[NDArray[floating | complexfloating]]],
|
||||
J: ArrayLike,
|
||||
epsilon: ArrayLike,
|
||||
mu: ArrayLike | None = None,
|
||||
pec: ArrayLike | None = None,
|
||||
pmc: ArrayLike | None = None,
|
||||
adjoint: bool = False,
|
||||
max_iters: int = 40000,
|
||||
err_threshold: float = 1e-6,
|
||||
context: pyopencl.Context | None = None,
|
||||
) -> NDArray:
|
||||
"""
|
||||
OpenCL FDFD solver using the iterative conjugate gradient (cg) method
|
||||
and implementing the diagonalized E-field wave operator directly in
|
||||
OpenCL.
|
||||
|
||||
All ndarray arguments should be 1D arrays. To linearize a list of 3 3D ndarrays,
|
||||
either use fdfd_tools.vec() or numpy:
|
||||
either use meanas.fdmath.vec() or numpy:
|
||||
f_1D = numpy.hstack(tuple((fi.flatten(order='F') for fi in [f_x, f_y, f_z])))
|
||||
|
||||
:param omega: Complex frequency to solve at.
|
||||
:param dxes: [[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]] (complex cell sizes)
|
||||
:param J: Electric current distribution (at E-field locations)
|
||||
:param epsilon: Dielectric constant distribution (at E-field locations)
|
||||
:param mu: Magnetic permeability distribution (at H-field locations)
|
||||
:param pec: Perfect electric conductor distribution
|
||||
(at E-field locations; non-zero value indicates PEC is present)
|
||||
:param pmc: Perfect magnetic conductor distribution
|
||||
(at H-field locations; non-zero value indicates PMC is present)
|
||||
:param adjoint: If true, solves the adjoint problem.
|
||||
:param max_iters: Maximum number of iterations. Default 40,000.
|
||||
:param err_threshold: If (r @ r.conj()) / norm(1j * omega * J) < err_threshold, success.
|
||||
Default 1e-6.
|
||||
:param context: PyOpenCL context to run in. If not given, construct a new context.
|
||||
:return: E-field which solves the system. Returned even if we did not converge.
|
||||
"""
|
||||
Args:
|
||||
omega: Complex frequency to solve at.
|
||||
dxes: [[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]] (complex cell sizes)
|
||||
J: Electric current distribution (at E-field locations)
|
||||
epsilon: Dielectric constant distribution (at E-field locations)
|
||||
mu: Magnetic permeability distribution (at H-field locations)
|
||||
pec: Perfect electric conductor distribution
|
||||
(at E-field locations; non-zero value indicates PEC is present)
|
||||
pmc: Perfect magnetic conductor distribution
|
||||
(at H-field locations; non-zero value indicates PMC is present)
|
||||
adjoint: If true, solves the adjoint problem.
|
||||
max_iters: Maximum number of iterations. Default 40,000.
|
||||
err_threshold: If (r @ r.conj()) / norm(1j * omega * J) < err_threshold, success.
|
||||
Default 1e-6.
|
||||
context: PyOpenCL context to run in. If not given, construct a new context.
|
||||
|
||||
Returns:
|
||||
E-field which solves the system. Returned even if we did not converge.
|
||||
"""
|
||||
start_time = time.perf_counter()
|
||||
|
||||
b = -1j * omega * J
|
||||
shape = [dd.size for dd in dxes[0]]
|
||||
|
||||
shape = [d.size for d in dxes[0]]
|
||||
b = -1j * omega * numpy.asarray(J)
|
||||
|
||||
'''
|
||||
** In this comment, I use the following notation:
|
||||
@ -96,30 +97,29 @@ def cg_solver(omega: complex,
|
||||
We can accomplish all this simply by conjugating everything (except J) and
|
||||
reversing the order of L and R
|
||||
'''
|
||||
epsilon = numpy.asarray(epsilon)
|
||||
|
||||
if adjoint:
|
||||
# Conjugate everything
|
||||
dxes = [[numpy.conj(d) for d in dd] for dd in dxes]
|
||||
dxes = [[numpy.conj(dd) for dd in dds] for dds in dxes]
|
||||
omega = numpy.conj(omega)
|
||||
epsilon = numpy.conj(epsilon)
|
||||
if mu is not None:
|
||||
mu = numpy.conj(mu)
|
||||
assert isinstance(epsilon, NDArray[floating] | NDArray[complexfloating])
|
||||
|
||||
L, R = fdfd_tools.operators.e_full_preconditioners(dxes)
|
||||
L, R = meanas.fdfd.operators.e_full_preconditioners(dxes)
|
||||
b_preconditioned = (R if adjoint else L) @ b
|
||||
|
||||
if adjoint:
|
||||
b_preconditioned = R @ b
|
||||
else:
|
||||
b_preconditioned = L @ b
|
||||
|
||||
'''
|
||||
Allocate GPU memory and load in data
|
||||
'''
|
||||
#
|
||||
# Allocate GPU memory and load in data
|
||||
#
|
||||
if context is None:
|
||||
context = pyopencl.create_some_context(interactive=True)
|
||||
|
||||
queue = pyopencl.CommandQueue(context)
|
||||
|
||||
def load_field(v, dtype=numpy.complex128):
|
||||
def load_field(v: NDArray[complexfloating | floating], dtype: type = numpy.complex128) -> pyopencl.array.Array:
|
||||
return pyopencl.array.to_device(queue, v.astype(dtype))
|
||||
|
||||
r = load_field(b_preconditioned) # load preconditioned b into r
|
||||
@ -132,30 +132,31 @@ def cg_solver(omega: complex,
|
||||
rho = 1.0 + 0j
|
||||
errs = []
|
||||
|
||||
inv_dxes = [[load_field(1 / d) for d in dd] for dd in dxes]
|
||||
oeps = load_field(-omega ** 2 * epsilon)
|
||||
inv_dxes = [[load_field(1 / numpy.asarray(dd)) for dd in dds] for dds in dxes]
|
||||
oeps = load_field(-omega * omega * epsilon)
|
||||
Pl = load_field(L.diagonal())
|
||||
Pr = load_field(R.diagonal())
|
||||
|
||||
if mu is None:
|
||||
invm = load_field(numpy.array([]))
|
||||
else:
|
||||
invm = load_field(1 / mu)
|
||||
invm = load_field(1 / numpy.asarray(mu))
|
||||
mu = numpy.asarray(mu)
|
||||
|
||||
if pec is None:
|
||||
gpec = load_field(numpy.array([]), dtype=numpy.int8)
|
||||
else:
|
||||
gpec = load_field(pec.astype(bool), dtype=numpy.int8)
|
||||
gpec = load_field(numpy.asarray(pec, dtype=bool), dtype=numpy.int8)
|
||||
|
||||
if pmc is None:
|
||||
gpmc = load_field(numpy.array([]), dtype=numpy.int8)
|
||||
else:
|
||||
gpmc = load_field(pmc.astype(bool), dtype=numpy.int8)
|
||||
gpmc = load_field(numpy.asarray(pmc, dtype=bool), dtype=numpy.int8)
|
||||
|
||||
'''
|
||||
Generate OpenCL kernels
|
||||
'''
|
||||
has_mu, has_pec, has_pmc = [q is not None for q in (mu, pec, pmc)]
|
||||
#
|
||||
# Generate OpenCL kernels
|
||||
#
|
||||
has_mu, has_pec, has_pmc = (qq is not None for qq in (mu, pec, pmc))
|
||||
|
||||
a_step_full = ops.create_a(context, shape, has_mu, has_pec, has_pmc)
|
||||
xr_step = ops.create_xr_step(context)
|
||||
@ -163,23 +164,28 @@ def cg_solver(omega: complex,
|
||||
p_step = ops.create_p_step(context)
|
||||
dot = ops.create_dot(context)
|
||||
|
||||
def a_step(E, H, p, events):
|
||||
def a_step(
|
||||
E: pyopencl.array.Array,
|
||||
H: pyopencl.array.Array,
|
||||
p: pyopencl.array.Array,
|
||||
events: list[pyopencl.Event],
|
||||
) -> list[pyopencl.Event]:
|
||||
return a_step_full(E, H, p, inv_dxes, oeps, invm, gpec, gpmc, Pl, Pr, events)
|
||||
|
||||
'''
|
||||
Start the solve
|
||||
'''
|
||||
#
|
||||
# Start the solve
|
||||
#
|
||||
start_time2 = time.perf_counter()
|
||||
|
||||
_, err2 = rhoerr_step(r, [])
|
||||
b_norm = numpy.sqrt(err2)
|
||||
logging.debug('b_norm check: {}'.format(b_norm))
|
||||
logging.debug(f'b_norm check: {b_norm}')
|
||||
|
||||
success = False
|
||||
for k in range(max_iters):
|
||||
do_print = (k % 100 == 0)
|
||||
if do_print:
|
||||
logger.debug('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha))
|
||||
logger.debug(f'[{k:06d}] rho {rho:.4} alpha {alpha:4.4}')
|
||||
|
||||
rho_prev = rho
|
||||
e = xr_step(x, p, r, v, alpha, [])
|
||||
@ -188,7 +194,7 @@ def cg_solver(omega: complex,
|
||||
errs += [numpy.sqrt(err2) / b_norm]
|
||||
|
||||
if do_print:
|
||||
logger.debug('err {}'.format(errs[-1]))
|
||||
logger.debug(f'err {errs[-1]}')
|
||||
|
||||
if errs[-1] < err_threshold:
|
||||
success = True
|
||||
@ -199,32 +205,30 @@ def cg_solver(omega: complex,
|
||||
alpha = rho / dot(p, v, e)
|
||||
|
||||
if k % 1000 == 0:
|
||||
logger.info('iteration {}'.format(k))
|
||||
logger.info(f'iteration {k}')
|
||||
|
||||
'''
|
||||
Done solving
|
||||
'''
|
||||
#
|
||||
# Done solving
|
||||
#
|
||||
time_elapsed = time.perf_counter() - start_time
|
||||
|
||||
# Undo preconditioners
|
||||
if adjoint:
|
||||
x = (Pl * x).get()
|
||||
else:
|
||||
x = (Pr * x).get()
|
||||
x = ((Pl if adjoint else Pr) * x).get()
|
||||
|
||||
if success:
|
||||
logger.info('Solve success')
|
||||
else:
|
||||
logger.warning('Solve failure')
|
||||
logger.info('{} iterations in {} sec: {} iterations/sec \
|
||||
'.format(k, time_elapsed, k / time_elapsed))
|
||||
logger.debug('final error {}'.format(errs[-1]))
|
||||
logger.debug('overhead {} sec'.format(start_time2 - start_time))
|
||||
logger.info(f'{k} iterations in {time_elapsed} sec: {k / time_elapsed} iterations/sec')
|
||||
logger.debug(f'final error {errs[-1]}')
|
||||
logger.debug(f'overhead {start_time2 - start_time} sec')
|
||||
|
||||
A0 = fdfd_tools.operators.e_full(omega, dxes, epsilon, mu).tocsr()
|
||||
A0 = meanas.fdfd.operators.e_full(omega, dxes, epsilon, mu).tocsr()
|
||||
if adjoint:
|
||||
# Remember we conjugated all the contents of A earlier
|
||||
A0 = A0.T
|
||||
logger.info('Post-everything residual: {}'.format(norm(A0 @ x - b) / norm(b)))
|
||||
|
||||
residual = norm(A0 @ x - b) / norm(b)
|
||||
logger.info(f'Post-everything residual: {residual}')
|
||||
return x
|
||||
|
||||
|
@ -7,10 +7,11 @@ kernels for use by the other solvers.
|
||||
See kernels/ for any of the .cl files loaded in this file.
|
||||
"""
|
||||
|
||||
from typing import List, Callable
|
||||
from collections.abc import Callable, Sequence
|
||||
import logging
|
||||
|
||||
import numpy
|
||||
from numpy.typing import ArrayLike
|
||||
import jinja2
|
||||
|
||||
import pyopencl
|
||||
@ -19,61 +20,77 @@ from pyopencl.elementwise import ElementwiseKernel
|
||||
from pyopencl.reduction import ReductionKernel
|
||||
|
||||
|
||||
from .csr import CSRMatrix
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class FDFDError(Exception):
|
||||
""" Custom error for opencl_fdfd """
|
||||
pass
|
||||
|
||||
# Create jinja2 env on module load
|
||||
jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__, 'kernels'))
|
||||
|
||||
# Return type for the create_opname(...) functions
|
||||
operation = Callable[..., List[pyopencl.Event]]
|
||||
operation = Callable[..., list[pyopencl.Event]]
|
||||
|
||||
|
||||
def type_to_C(float_type: numpy.float32 or numpy.float64) -> str:
|
||||
def type_to_C(
|
||||
float_type: type[numpy.floating | numpy.complexfloating],
|
||||
) -> str:
|
||||
"""
|
||||
Returns a string corresponding to the C equivalent of a numpy type.
|
||||
|
||||
:param float_type: numpy type: float32, float64, complex64, complex128
|
||||
:return: string containing the corresponding C type (eg. 'double')
|
||||
Args:
|
||||
float_type: numpy type: float32, float64, complex64, complex128
|
||||
|
||||
Returns:
|
||||
string containing the corresponding C type (eg. 'double')
|
||||
"""
|
||||
types = {
|
||||
numpy.float16: 'half',
|
||||
numpy.float32: 'float',
|
||||
numpy.float64: 'double',
|
||||
numpy.complex64: 'cfloat_t',
|
||||
numpy.complex128: 'cdouble_t',
|
||||
}
|
||||
if float_type not in types:
|
||||
raise Exception('Unsupported type')
|
||||
raise FDFDError('Unsupported type')
|
||||
|
||||
return types[float_type]
|
||||
|
||||
|
||||
# Type names
|
||||
ctype = type_to_C(numpy.complex128)
|
||||
ctype_bare = 'cdouble'
|
||||
|
||||
# Preamble for all OpenCL code
|
||||
preamble = '''
|
||||
preamble = f'''
|
||||
#define PYOPENCL_DEFINE_CDOUBLE
|
||||
#include <pyopencl-complex.h>
|
||||
|
||||
//Defines to clean up operation and type names
|
||||
#define ctype {ctype}_t
|
||||
#define zero {ctype}_new(0.0, 0.0)
|
||||
#define add {ctype}_add
|
||||
#define sub {ctype}_sub
|
||||
#define mul {ctype}_mul
|
||||
'''.format(ctype=ctype_bare)
|
||||
#define ctype {ctype_bare}_t
|
||||
#define zero {ctype_bare}_new(0.0, 0.0)
|
||||
#define add {ctype_bare}_add
|
||||
#define sub {ctype_bare}_sub
|
||||
#define mul {ctype_bare}_mul
|
||||
'''
|
||||
|
||||
|
||||
def ptrs(*args: str) -> List[str]:
|
||||
def ptrs(*args: str) -> list[str]:
|
||||
return [ctype + ' *' + s for s in args]
|
||||
|
||||
|
||||
def create_a(context: pyopencl.Context,
|
||||
shape: numpy.ndarray,
|
||||
mu: bool = False,
|
||||
pec: bool = False,
|
||||
pmc: bool = False,
|
||||
) -> operation:
|
||||
def create_a(
|
||||
context: pyopencl.Context,
|
||||
shape: ArrayLike,
|
||||
mu: bool = False,
|
||||
pec: bool = False,
|
||||
pmc: bool = False,
|
||||
) -> operation:
|
||||
"""
|
||||
Return a function which performs (A @ p), where A is the FDFD wave equation for E-field.
|
||||
|
||||
@ -94,12 +111,15 @@ def create_a(context: pyopencl.Context,
|
||||
|
||||
and returns a list of pyopencl.Event.
|
||||
|
||||
:param context: PyOpenCL context
|
||||
:param shape: Dimensions of the E-field
|
||||
:param mu: False iff (mu == 1) everywhere
|
||||
:param pec: False iff no PEC anywhere
|
||||
:param pmc: False iff no PMC anywhere
|
||||
:return: Function for computing (A @ p)
|
||||
Args:
|
||||
context: PyOpenCL context
|
||||
shape: Dimensions of the E-field
|
||||
mu: False iff (mu == 1) everywhere
|
||||
pec: False iff no PEC anywhere
|
||||
pmc: False iff no PMC anywhere
|
||||
|
||||
Returns:
|
||||
Function for computing (A @ p)
|
||||
"""
|
||||
|
||||
common_source = jinja_env.get_template('common.cl').render(shape=shape)
|
||||
@ -109,49 +129,71 @@ def create_a(context: pyopencl.Context,
|
||||
des = [ctype + ' *inv_de' + a for a in 'xyz']
|
||||
dhs = [ctype + ' *inv_dh' + a for a in 'xyz']
|
||||
|
||||
'''
|
||||
Convert p to initial E (ie, apply right preconditioner and PEC)
|
||||
'''
|
||||
#
|
||||
# Convert p to initial E (ie, apply right preconditioner and PEC)
|
||||
#
|
||||
p2e_source = jinja_env.get_template('p2e.cl').render(pec=pec)
|
||||
P2E_kernel = ElementwiseKernel(context,
|
||||
name='P2E',
|
||||
preamble=preamble,
|
||||
operation=p2e_source,
|
||||
arguments=', '.join(ptrs('E', 'p', 'Pr') + pec_arg))
|
||||
P2E_kernel = ElementwiseKernel(
|
||||
context,
|
||||
name='P2E',
|
||||
preamble=preamble,
|
||||
operation=p2e_source,
|
||||
arguments=', '.join(ptrs('E', 'p', 'Pr') + pec_arg),
|
||||
)
|
||||
|
||||
'''
|
||||
Calculate intermediate H from intermediate E
|
||||
'''
|
||||
e2h_source = jinja_env.get_template('e2h.cl').render(mu=mu,
|
||||
pmc=pmc,
|
||||
common_cl=common_source)
|
||||
E2H_kernel = ElementwiseKernel(context,
|
||||
name='E2H',
|
||||
preamble=preamble,
|
||||
operation=e2h_source,
|
||||
arguments=', '.join(ptrs('E', 'H', 'inv_mu') + pmc_arg + des))
|
||||
#
|
||||
# Calculate intermediate H from intermediate E
|
||||
#
|
||||
e2h_source = jinja_env.get_template('e2h.cl').render(
|
||||
mu=mu,
|
||||
pmc=pmc,
|
||||
common_cl=common_source,
|
||||
)
|
||||
E2H_kernel = ElementwiseKernel(
|
||||
context,
|
||||
name='E2H',
|
||||
preamble=preamble,
|
||||
operation=e2h_source,
|
||||
arguments=', '.join(ptrs('E', 'H', 'inv_mu') + pmc_arg + des),
|
||||
)
|
||||
|
||||
'''
|
||||
Calculate final E (including left preconditioner)
|
||||
'''
|
||||
h2e_source = jinja_env.get_template('h2e.cl').render(pec=pec,
|
||||
common_cl=common_source)
|
||||
H2E_kernel = ElementwiseKernel(context,
|
||||
name='H2E',
|
||||
preamble=preamble,
|
||||
operation=h2e_source,
|
||||
arguments=', '.join(ptrs('E', 'H', 'oeps', 'Pl') + pec_arg + dhs))
|
||||
#
|
||||
# Calculate final E (including left preconditioner)
|
||||
#
|
||||
h2e_source = jinja_env.get_template('h2e.cl').render(
|
||||
pec=pec,
|
||||
common_cl=common_source,
|
||||
)
|
||||
H2E_kernel = ElementwiseKernel(
|
||||
context,
|
||||
name='H2E',
|
||||
preamble=preamble,
|
||||
operation=h2e_source,
|
||||
arguments=', '.join(ptrs('E', 'H', 'oeps', 'Pl') + pec_arg + dhs),
|
||||
)
|
||||
|
||||
def spmv(E, H, p, idxes, oeps, inv_mu, pec, pmc, Pl, Pr, e):
|
||||
def spmv(
|
||||
E: pyopencl.array.Array,
|
||||
H: pyopencl.array.Array,
|
||||
p: pyopencl.array.Array,
|
||||
idxes: Sequence[Sequence[pyopencl.array.Array]],
|
||||
oeps: pyopencl.array.Array,
|
||||
inv_mu: pyopencl.array.Array | None,
|
||||
pec: pyopencl.array.Array | None,
|
||||
pmc: pyopencl.array.Array | None,
|
||||
Pl: pyopencl.array.Array,
|
||||
Pr: pyopencl.array.Array,
|
||||
e: list[pyopencl.Event],
|
||||
) -> list[pyopencl.Event]:
|
||||
e2 = P2E_kernel(E, p, Pr, pec, wait_for=e)
|
||||
e2 = E2H_kernel(E, H, inv_mu, pmc, *idxes[0], wait_for=[e2])
|
||||
e2 = H2E_kernel(E, H, oeps, Pl, pec, *idxes[1], wait_for=[e2])
|
||||
return [e2]
|
||||
|
||||
logger.debug('Preamble: \n{}'.format(preamble))
|
||||
logger.debug('p2e: \n{}'.format(p2e_source))
|
||||
logger.debug('e2h: \n{}'.format(e2h_source))
|
||||
logger.debug('h2e: \n{}'.format(h2e_source))
|
||||
logger.debug(f'Preamble: \n{preamble}')
|
||||
logger.debug(f'p2e: \n{p2e_source}')
|
||||
logger.debug(f'e2h: \n{e2h_source}')
|
||||
logger.debug(f'h2e: \n{h2e_source}')
|
||||
|
||||
return spmv
|
||||
|
||||
@ -167,8 +209,11 @@ def create_xr_step(context: pyopencl.Context) -> operation:
|
||||
after waiting for all in the list e
|
||||
and returns a list of pyopencl.Event
|
||||
|
||||
:param context: PyOpenCL context
|
||||
:return: Function for performing x and r updates
|
||||
Args:
|
||||
context: PyOpenCL context
|
||||
|
||||
Returns:
|
||||
Function for performing x and r updates
|
||||
"""
|
||||
update_xr_source = '''
|
||||
x[i] = add(x[i], mul(alpha, p[i]));
|
||||
@ -177,19 +222,28 @@ def create_xr_step(context: pyopencl.Context) -> operation:
|
||||
|
||||
xr_args = ', '.join(ptrs('x', 'p', 'r', 'v') + [ctype + ' alpha'])
|
||||
|
||||
xr_kernel = ElementwiseKernel(context,
|
||||
name='XR',
|
||||
preamble=preamble,
|
||||
operation=update_xr_source,
|
||||
arguments=xr_args)
|
||||
xr_kernel = ElementwiseKernel(
|
||||
context,
|
||||
name='XR',
|
||||
preamble=preamble,
|
||||
operation=update_xr_source,
|
||||
arguments=xr_args,
|
||||
)
|
||||
|
||||
def xr_update(x, p, r, v, alpha, e):
|
||||
def xr_update(
|
||||
x: pyopencl.array.Array,
|
||||
p: pyopencl.array.Array,
|
||||
r: pyopencl.array.Array,
|
||||
v: pyopencl.array.Array,
|
||||
alpha: complex,
|
||||
e: list[pyopencl.Event],
|
||||
) -> list[pyopencl.Event]:
|
||||
return [xr_kernel(x, p, r, v, alpha, wait_for=e)]
|
||||
|
||||
return xr_update
|
||||
|
||||
|
||||
def create_rhoerr_step(context: pyopencl.Context) -> operation:
|
||||
def create_rhoerr_step(context: pyopencl.Context) -> Callable[..., tuple[complex, complex]]:
|
||||
"""
|
||||
Return a function
|
||||
ri_update(r, e)
|
||||
@ -200,8 +254,11 @@ def create_rhoerr_step(context: pyopencl.Context) -> operation:
|
||||
after waiting for all pyopencl.Event in the list e
|
||||
and returns a list of pyopencl.Event
|
||||
|
||||
:param context: PyOpenCL context
|
||||
:return: Function for performing x and r updates
|
||||
Args:
|
||||
context: PyOpenCL context
|
||||
|
||||
Returns:
|
||||
Function for performing x and r updates
|
||||
"""
|
||||
|
||||
update_ri_source = '''
|
||||
@ -213,18 +270,20 @@ def create_rhoerr_step(context: pyopencl.Context) -> operation:
|
||||
# Use a vector type (double3) to make the reduction simpler
|
||||
ri_dtype = pyopencl.array.vec.double3
|
||||
|
||||
ri_kernel = ReductionKernel(context,
|
||||
name='RHOERR',
|
||||
preamble=preamble,
|
||||
dtype_out=ri_dtype,
|
||||
neutral='(double3)(0.0, 0.0, 0.0)',
|
||||
map_expr=update_ri_source,
|
||||
reduce_expr='a+b',
|
||||
arguments=ctype + ' *r')
|
||||
ri_kernel = ReductionKernel(
|
||||
context,
|
||||
name='RHOERR',
|
||||
preamble=preamble,
|
||||
dtype_out=ri_dtype,
|
||||
neutral='(double3)(0.0, 0.0, 0.0)',
|
||||
map_expr=update_ri_source,
|
||||
reduce_expr='a+b',
|
||||
arguments=ctype + ' *r',
|
||||
)
|
||||
|
||||
def ri_update(r, e):
|
||||
def ri_update(r: pyopencl.array.Array, e: list[pyopencl.Event]) -> tuple[complex, complex]:
|
||||
g = ri_kernel(r, wait_for=e).astype(ri_dtype).get()
|
||||
rr, ri, ii = [g[q] for q in 'xyz']
|
||||
rr, ri, ii = (g[qq] for qq in 'xyz')
|
||||
rho = rr + 2j * ri - ii
|
||||
err = rr + ii
|
||||
return rho, err
|
||||
@ -242,48 +301,66 @@ def create_p_step(context: pyopencl.Context) -> operation:
|
||||
after waiting for all pyopencl.Event in the list e
|
||||
and returns a list of pyopencl.Event
|
||||
|
||||
:param context: PyOpenCL context
|
||||
:return: Function for performing the p update
|
||||
Args:
|
||||
context: PyOpenCL context
|
||||
|
||||
Returns:
|
||||
Function for performing the p update
|
||||
"""
|
||||
update_p_source = '''
|
||||
p[i] = add(r[i], mul(beta, p[i]));
|
||||
'''
|
||||
p_args = ptrs('p', 'r') + [ctype + ' beta']
|
||||
|
||||
p_kernel = ElementwiseKernel(context,
|
||||
name='P',
|
||||
preamble=preamble,
|
||||
operation=update_p_source,
|
||||
arguments=', '.join(p_args))
|
||||
p_kernel = ElementwiseKernel(
|
||||
context,
|
||||
name='P',
|
||||
preamble=preamble,
|
||||
operation=update_p_source,
|
||||
arguments=', '.join(p_args),
|
||||
)
|
||||
|
||||
def p_update(p, r, beta, e):
|
||||
def p_update(
|
||||
p: pyopencl.array.Array,
|
||||
r: pyopencl.array.Array,
|
||||
beta: complex,
|
||||
e: list[pyopencl.Event]) -> list[pyopencl.Event]:
|
||||
return [p_kernel(p, r, beta, wait_for=e)]
|
||||
|
||||
return p_update
|
||||
|
||||
|
||||
def create_dot(context: pyopencl.Context) -> operation:
|
||||
def create_dot(context: pyopencl.Context) -> Callable[..., complex]:
|
||||
"""
|
||||
Return a function for performing the dot product
|
||||
p @ v
|
||||
with the signature
|
||||
dot(p, v, e) -> float
|
||||
dot(p, v, e) -> complex
|
||||
|
||||
:param context: PyOpenCL context
|
||||
:return: Function for performing the dot product
|
||||
Args:
|
||||
context: PyOpenCL context
|
||||
|
||||
Returns:
|
||||
Function for performing the dot product
|
||||
"""
|
||||
dot_dtype = numpy.complex128
|
||||
|
||||
dot_kernel = ReductionKernel(context,
|
||||
name='dot',
|
||||
preamble=preamble,
|
||||
dtype_out=dot_dtype,
|
||||
neutral='zero',
|
||||
map_expr='mul(p[i], v[i])',
|
||||
reduce_expr='add(a, b)',
|
||||
arguments=ptrs('p', 'v'))
|
||||
dot_kernel = ReductionKernel(
|
||||
context,
|
||||
name='dot',
|
||||
preamble=preamble,
|
||||
dtype_out=dot_dtype,
|
||||
neutral='zero',
|
||||
map_expr='mul(p[i], v[i])',
|
||||
reduce_expr='add(a, b)',
|
||||
arguments=ptrs('p', 'v'),
|
||||
)
|
||||
|
||||
def dot(p, v, e):
|
||||
def dot(
|
||||
p: pyopencl.array.Array,
|
||||
v: pyopencl.array.Array,
|
||||
e: list[pyopencl.Event],
|
||||
) -> complex:
|
||||
g = dot_kernel(p, v, wait_for=e)
|
||||
return g.get()
|
||||
|
||||
@ -304,8 +381,11 @@ def create_a_csr(context: pyopencl.Context) -> operation:
|
||||
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
|
||||
Args:
|
||||
context: PyOpenCL context
|
||||
|
||||
Returns:
|
||||
Function for sparse (M @ v) operation where M is in CSR format
|
||||
"""
|
||||
spmv_source = '''
|
||||
int start = m_row_ptr[i];
|
||||
@ -326,13 +406,20 @@ def create_a_csr(context: pyopencl.Context) -> operation:
|
||||
m_args = 'int *m_row_ptr, int *m_col_ind, ' + ctype + ' *m_data'
|
||||
v_in_args = ctype + ' *v_in'
|
||||
|
||||
spmv_kernel = ElementwiseKernel(context,
|
||||
name='csr_spmv',
|
||||
preamble=preamble,
|
||||
operation=spmv_source,
|
||||
arguments=', '.join((v_out_args, m_args, v_in_args)))
|
||||
spmv_kernel = ElementwiseKernel(
|
||||
context,
|
||||
name='csr_spmv',
|
||||
preamble=preamble,
|
||||
operation=spmv_source,
|
||||
arguments=', '.join((v_out_args, m_args, v_in_args)),
|
||||
)
|
||||
|
||||
def spmv(v_out, m, v_in, e):
|
||||
def spmv(
|
||||
v_out: pyopencl.array.Array,
|
||||
m: CSRMatrix,
|
||||
v_in: pyopencl.array.Array,
|
||||
e: list[pyopencl.Event],
|
||||
) -> list[pyopencl.Event]:
|
||||
return [spmv_kernel(v_out, m.row_ptr, m.col_ind, m.data, v_in, wait_for=e)]
|
||||
|
||||
return spmv
|
||||
|
0
opencl_fdfd/py.typed
Normal file
0
opencl_fdfd/py.typed
Normal file
96
pyproject.toml
Normal file
96
pyproject.toml
Normal file
@ -0,0 +1,96 @@
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[project]
|
||||
name = "opencl_fdfd"
|
||||
description = "OpenCL FDFD solver"
|
||||
readme = "README.md"
|
||||
license = { file = "LICENSE.md" }
|
||||
authors = [
|
||||
{ name="Jan Petykiewicz", email="jan@mpxd.net" },
|
||||
]
|
||||
homepage = "https://mpxd.net/code/jan/opencl_fdfd"
|
||||
repository = "https://mpxd.net/code/jan/opencl_fdfd"
|
||||
keywords = [
|
||||
"FDFD",
|
||||
"finite",
|
||||
"difference",
|
||||
"frequency",
|
||||
"domain",
|
||||
"simulation",
|
||||
"optics",
|
||||
"electromagnetic",
|
||||
"dielectric",
|
||||
"PML",
|
||||
"solver",
|
||||
"FDTD",
|
||||
]
|
||||
classifiers = [
|
||||
"Programming Language :: Python :: 3",
|
||||
"Development Status :: 4 - Beta",
|
||||
"Intended Audience :: Developers",
|
||||
"Intended Audience :: Manufacturing",
|
||||
"Intended Audience :: Science/Research",
|
||||
"License :: OSI Approved :: GNU Affero General Public License v3",
|
||||
"Topic :: Scientific/Engineering",
|
||||
]
|
||||
requires-python = ">=3.11"
|
||||
dynamic = ["version"]
|
||||
dependencies = [
|
||||
"numpy>=1.26",
|
||||
"pyopencl",
|
||||
"jinja2",
|
||||
"meanas>=0.5",
|
||||
]
|
||||
|
||||
[tool.hatch.version]
|
||||
path = "opencl_fdfd/__init__.py"
|
||||
|
||||
|
||||
[tool.ruff]
|
||||
exclude = [
|
||||
".git",
|
||||
"dist",
|
||||
]
|
||||
line-length = 145
|
||||
indent-width = 4
|
||||
lint.dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
|
||||
lint.select = [
|
||||
"NPY", "E", "F", "W", "B", "ANN", "UP", "SLOT", "SIM", "LOG",
|
||||
"C4", "ISC", "PIE", "PT", "RET", "TCH", "PTH", "INT",
|
||||
"ARG", "PL", "R", "TRY",
|
||||
"G010", "G101", "G201", "G202",
|
||||
"Q002", "Q003", "Q004",
|
||||
]
|
||||
lint.ignore = [
|
||||
#"ANN001", # No annotation
|
||||
"ANN002", # *args
|
||||
"ANN003", # **kwargs
|
||||
"ANN401", # Any
|
||||
"ANN101", # self: Self
|
||||
"SIM108", # single-line if / else assignment
|
||||
"RET504", # x=y+z; return x
|
||||
"PIE790", # unnecessary pass
|
||||
"ISC003", # non-implicit string concatenation
|
||||
"C408", # dict(x=y) instead of {'x': y}
|
||||
"PLR09", # Too many xxx
|
||||
"PLR2004", # magic number
|
||||
"PLC0414", # import x as x
|
||||
"TRY003", # Long exception message
|
||||
]
|
||||
|
||||
|
||||
[[tool.mypy.overrides]]
|
||||
module = [
|
||||
"scipy",
|
||||
"scipy.optimize",
|
||||
"scipy.linalg",
|
||||
"scipy.sparse",
|
||||
"scipy.sparse.linalg",
|
||||
"pyopencl",
|
||||
"pyopencl.array",
|
||||
"pyopencl.elementwise",
|
||||
"pyopencl.reduction",
|
||||
]
|
||||
ignore_missing_imports = true
|
30
setup.py
30
setup.py
@ -1,30 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
from setuptools import setup, find_packages
|
||||
import opencl_fdfd
|
||||
|
||||
with open('README.md', 'r') as f:
|
||||
long_description = f.read()
|
||||
|
||||
setup(name='opencl_fdfd',
|
||||
version=opencl_fdfd.version,
|
||||
description='OpenCL FDFD solver',
|
||||
long_description=long_description,
|
||||
long_description_content_type='text/markdown',
|
||||
author='Jan Petykiewicz',
|
||||
author_email='anewusername@gmail.com',
|
||||
url='https://mpxd.net/code/jan/opencl_fdfd',
|
||||
packages=find_packages(),
|
||||
package_data={
|
||||
'opencl_fdfd': ['kernels/*']
|
||||
},
|
||||
install_requires=[
|
||||
'numpy',
|
||||
'pyopencl',
|
||||
'jinja2',
|
||||
'fdfd_tools>=0.3',
|
||||
],
|
||||
extras_require={
|
||||
},
|
||||
)
|
||||
|
Loading…
Reference in New Issue
Block a user