forked from jan/opencl_fdfd
improve type annotations, formatting, comment styles
This commit is contained in:
parent
81bb1dd2c0
commit
efeb29479b
@ -14,14 +14,16 @@ 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 Dict, Any, Optional
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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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import pyopencl
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import pyopencl.array
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import scipy
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import meanas.fdfd.solvers
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@ -33,39 +35,45 @@ __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: Optional[pyopencl.Context] = None,
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queue: Optional[pyopencl.CommandQueue] = None,
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) -> 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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: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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@ -151,29 +159,37 @@ def cg(A: 'scipy.sparse.csr_matrix',
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return x
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def fdfd_cg_solver(solver_opts: Dict[str, Any] = None,
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**fdfd_args
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) -> numpy.ndarray:
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def fdfd_cg_solver(
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solver_opts: Optional[Dict[str, Any]] = None,
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**fdfd_args
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) -> 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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Calls meanas.fdfd.solvers.generic(**fdfd_args,
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matrix_solver=opencl_fdfd.csr.cg,
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matrix_solver_opts=solver_opts)
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Calls meanas.fdfd.solvers.generic(
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**fdfd_args,
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matrix_solver=opencl_fdfd.csr.cg,
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matrix_solver_opts=solver_opts,
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)
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:param solver_opts: Passed as matrix_solver_opts to fdfd_tools.solver.generic(...).
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Default {}.
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:param fdfd_args: Passed as **fdfd_args to fdfd_tools.solver.generic(...).
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Should include all of the arguments **except** matrix_solver and matrix_solver_opts
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:return: E-field which solves the system.
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Args:
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solver_opts: Passed as matrix_solver_opts to fdfd_tools.solver.generic(...).
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Default {}.
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fdfd_args: Passed as **fdfd_args to fdfd_tools.solver.generic(...).
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Should include all of the arguments **except** matrix_solver and matrix_solver_opts
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Returns:
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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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x = meanas.fdfd.solvers.generic(matrix_solver=cg,
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matrix_solver_opts=solver_opts,
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**fdfd_args)
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x = meanas.fdfd.solvers.generic(
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matrix_solver=cg,
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matrix_solver_opts=solver_opts,
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**fdfd_args,
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)
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return x
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@ -6,11 +6,12 @@ operator implemented directly as OpenCL arithmetic (rather than as
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a matrix).
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"""
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from typing import List
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from typing import List, Optional, cast
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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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import pyopencl
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import pyopencl.array
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@ -25,18 +26,19 @@ __author__ = 'Jan Petykiewicz'
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logger = logging.getLogger(__name__)
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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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) -> numpy.ndarray:
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def cg_solver(
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omega: complex,
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dxes: List[List[NDArray]],
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J: ArrayLike,
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epsilon: ArrayLike,
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mu: Optional[ArrayLike] = None,
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pec: Optional[ArrayLike] = None,
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pmc: Optional[ArrayLike] = 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: Optional[pyopencl.Context] = None,
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) -> 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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@ -46,28 +48,30 @@ def cg_solver(omega: complex,
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either use meanas.fdmath.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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:return: E-field which solves the system. Returned even if we did not converge.
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"""
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Args:
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omega: Complex frequency to solve at.
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dxes: [[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]] (complex cell sizes)
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J: Electric current distribution (at E-field locations)
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epsilon: Dielectric constant distribution (at E-field locations)
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mu: Magnetic permeability distribution (at H-field locations)
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pec: Perfect electric conductor distribution
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(at E-field locations; non-zero value indicates PEC is present)
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pmc: Perfect magnetic conductor distribution
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(at H-field locations; non-zero value indicates PMC is present)
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adjoint: If true, solves the adjoint problem.
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max_iters: Maximum number of iterations. Default 40,000.
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err_threshold: If (r @ r.conj()) / norm(1j * omega * J) < err_threshold, success.
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Default 1e-6.
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context: PyOpenCL context to run in. If not given, construct a new context.
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Returns:
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E-field which solves the system. Returned even if we did not converge.
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"""
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start_time = time.perf_counter()
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b = -1j * omega * J
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shape = [dd.size for dd in dxes[0]]
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shape = [d.size for d in dxes[0]]
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b = -1j * omega * numpy.array(J, copy=False)
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'''
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** In this comment, I use the following notation:
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@ -96,9 +100,10 @@ def cg_solver(omega: complex,
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We can accomplish all this simply by conjugating everything (except J) and
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reversing the order of L and R
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'''
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epsilon = numpy.array(epsilon, copy=False)
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if adjoint:
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# Conjugate everything
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dxes = [[numpy.conj(d) for d in dd] for dd in dxes]
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dxes = [[numpy.conj(dd) for dd in dds] for dds in dxes]
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omega = numpy.conj(omega)
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epsilon = numpy.conj(epsilon)
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if mu is not None:
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@ -132,7 +137,7 @@ def cg_solver(omega: complex,
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rho = 1.0 + 0j
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errs = []
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inv_dxes = [[load_field(1 / d) for d in dd] for dd in dxes]
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inv_dxes = [[load_field(1 / numpy.array(dd, copy=False)) for dd in dds] for dds in dxes]
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oeps = load_field(-omega ** 2 * epsilon)
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Pl = load_field(L.diagonal())
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Pr = load_field(R.diagonal())
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@ -140,17 +145,18 @@ def cg_solver(omega: complex,
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if mu is None:
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invm = load_field(numpy.array([]))
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else:
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invm = load_field(1 / mu)
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invm = load_field(1 / numpy.array(mu, copy=False))
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mu = numpy.array(mu, copy=False)
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if pec is None:
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gpec = load_field(numpy.array([]), dtype=numpy.int8)
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else:
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gpec = load_field(pec.astype(bool), dtype=numpy.int8)
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gpec = load_field(numpy.array(pec, dtype=bool, copy=False), dtype=numpy.int8)
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if pmc is None:
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gpmc = load_field(numpy.array([]), dtype=numpy.int8)
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else:
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gpmc = load_field(pmc.astype(bool), dtype=numpy.int8)
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gpmc = load_field(numpy.array(pmc, dtype=bool, copy=False), dtype=numpy.int8)
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'''
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Generate OpenCL kernels
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@ -7,10 +7,11 @@ kernels for use by the other solvers.
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See kernels/ for any of the .cl files loaded in this file.
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"""
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from typing import List, Callable
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from typing import List, Callable, Union, Type, Sequence, Optional, Tuple
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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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import jinja2
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import pyopencl
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@ -28,12 +29,17 @@ jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__, 'kernels'))
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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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def type_to_C(
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float_type: Type,
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) -> str:
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"""
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Returns a string corresponding to the C equivalent of a numpy type.
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:param float_type: numpy type: float32, float64, complex64, complex128
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:return: string containing the corresponding C type (eg. 'double')
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Args:
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float_type: numpy type: float32, float64, complex64, complex128
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Returns:
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string containing the corresponding C type (eg. 'double')
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"""
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types = {
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numpy.float32: 'float',
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@ -68,12 +74,13 @@ 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: 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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def create_a(
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context: pyopencl.Context,
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shape: ArrayLike,
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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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@ -94,12 +101,15 @@ def create_a(context: pyopencl.Context,
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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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Args:
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context: PyOpenCL context
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shape: Dimensions of the E-field
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mu: False iff (mu == 1) everywhere
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pec: False iff no PEC anywhere
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pmc: False iff no PMC anywhere
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Returns:
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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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@ -113,45 +123,67 @@ def create_a(context: pyopencl.Context,
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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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preamble=preamble,
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operation=p2e_source,
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arguments=', '.join(ptrs('E', 'p', 'Pr') + pec_arg))
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P2E_kernel = ElementwiseKernel(
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context,
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name='P2E',
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preamble=preamble,
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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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'''
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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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E2H_kernel = ElementwiseKernel(context,
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name='E2H',
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preamble=preamble,
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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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e2h_source = jinja_env.get_template('e2h.cl').render(
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mu=mu,
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pmc=pmc,
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common_cl=common_source,
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)
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E2H_kernel = ElementwiseKernel(
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context,
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name='E2H',
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preamble=preamble,
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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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'''
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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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name='H2E',
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preamble=preamble,
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operation=h2e_source,
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arguments=', '.join(ptrs('E', 'H', 'oeps', 'Pl') + pec_arg + dhs))
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h2e_source = jinja_env.get_template('h2e.cl').render(
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pec=pec,
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common_cl=common_source,
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)
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H2E_kernel = ElementwiseKernel(
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context,
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name='H2E',
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preamble=preamble,
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operation=h2e_source,
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arguments=', '.join(ptrs('E', 'H', 'oeps', 'Pl') + pec_arg + dhs),
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)
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def spmv(E, H, p, idxes, oeps, inv_mu, pec, pmc, Pl, Pr, e):
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def spmv(
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E: pyopencl.array.Array,
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H: pyopencl.array.Array,
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p: pyopencl.array.Array,
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idxes: Sequence[Sequence[pyopencl.array.Array]],
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oeps: pyopencl.array.Array,
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inv_mu: Optional[pyopencl.array.Array],
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pec: Optional[pyopencl.array.Array],
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pmc: Optional[pyopencl.array.Array],
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Pl: pyopencl.array.Array,
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Pr: pyopencl.array.Array,
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e: List[pyopencl.Event],
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) -> List[pyopencl.Event]:
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e2 = P2E_kernel(E, p, Pr, pec, wait_for=e)
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e2 = E2H_kernel(E, H, inv_mu, pmc, *idxes[0], wait_for=[e2])
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e2 = H2E_kernel(E, H, oeps, Pl, pec, *idxes[1], wait_for=[e2])
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return [e2]
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logger.debug('Preamble: \n{}'.format(preamble))
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logger.debug('p2e: \n{}'.format(p2e_source))
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logger.debug('e2h: \n{}'.format(e2h_source))
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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 +199,11 @@ 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 +212,28 @@ 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 +244,11 @@ 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 = '''
|
||||
@ -213,16 +260,18 @@ 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']
|
||||
rho = rr + 2j * ri - ii
|
||||
@ -242,48 +291,66 @@ 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 +371,11 @@ 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 +396,20 @@ 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,
|
||||
m,
|
||||
v_in,
|
||||
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
|
||||
|
||||
Loading…
Reference in New Issue
Block a user