Source code for pyfr.backends.opencl.provider

# -*- coding: utf-8 -*-

import numpy as np
import pyopencl as cl

from pyfr.backends.base import (BaseKernelProvider,
                                BasePointwiseKernelProvider, ComputeKernel)
import pyfr.backends.opencl.generator as generator
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 = generator.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.getliteral('backend-opencl', 'local-size-2d', '128, 1') # Global work size gs = tuple(dims[::-1]) class PointwiseKernel(ComputeKernel): def run(self, queue, **kwargs): kwargs = {k: float(v) for k, v in kwargs.items()} narglst = [kwargs.get(ka, ka) for ka in arglst] narglst = [getattr(arg, 'data', arg) for arg in narglst] fun(queue.cl_queue_comp, gs, ls, *narglst) return PointwiseKernel()