# -*- coding: utf-8 -*-
import numpy as np
import pyopencl as cl
from pyfr.backends.base import (BaseKernelProvider,
BasePointwiseKernelProvider, ComputeKernel)
from pyfr.backends.opencl.generator import OpenCLKernelGenerator
from pyfr.util import memoize
class OpenCLKernelProvider(BaseKernelProvider):
@memoize
def _build_kernel(self, name, src, argtypes):
# Compile the source code
prg = cl.Program(self.backend.ctx, src)
prg.build(['-cl-fast-relaxed-math'])
# Retrieve the kernel
kern = getattr(prg, name)
# Set the argument types
dtypes = [t if t != np.intp else None for t in argtypes]
kern.set_scalar_arg_dtypes(dtypes)
return kern
[docs]class OpenCLPointwiseKernelProvider(OpenCLKernelProvider,
BasePointwiseKernelProvider):
kernel_generator_cls = OpenCLKernelGenerator
[docs] def _instantiate_kernel(self, dims, fun, arglst):
cfg = self.backend.cfg
# Determine the local work size
if len(dims) == 1:
ls = (cfg.getint('backend-opencl', 'local-size-1d', '64'),)
else:
ls = (cfg.getint('backend-opencl', 'local-size-2d', '128'),)
# Global work size
gs = tuple(gi - gi % -li for gi, li in zip(dims[::-1], ls))
class PointwiseKernel(ComputeKernel):
if any(isinstance(arg, str) for arg in arglst):
def run(self, queue, **kwargs):
narglst = [kwargs.get(ka, ka) for ka in arglst]
narglst = [getattr(arg, 'data', arg) for arg in narglst]
fun(queue.cmd_q_comp, gs, ls, *narglst)
else:
def run(self, queue, **kwargs):
narglst = [getattr(arg, 'data', arg) for arg in arglst]
fun(queue.cmd_q_comp, gs, ls, *narglst)
return PointwiseKernel()