# -*- coding: utf-8 -*-
import re
from pyfr.backends.base import BaseBackend
from pyfr.mpiutil import get_local_rank
[docs]class CUDABackend(BaseBackend):
name = 'cuda'
blocks = False
def __init__(self, cfg):
super().__init__(cfg)
from pyfr.backends.cuda.compiler import NVRTC
from pyfr.backends.cuda.driver import CUDA, CUDAError
# Load and wrap CUDA and NVRTC
self.cuda = CUDA()
self.nvrtc = NVRTC()
# Get the desired CUDA device
devid = cfg.get('backend-cuda', 'device-id', 'round-robin')
if not re.match(r'(round-robin|local-rank|\d+)$', devid):
raise ValueError('Invalid device-id')
# For round-robin try each device until we find one that works
if devid == 'round-robin':
for i in range(self.cuda.device_count()):
try:
self.cuda.set_device(i)
break
except CUDAError:
pass
else:
raise RuntimeError('Unable to create a CUDA context')
elif devid == 'local-rank':
self.cuda.set_device(get_local_rank())
else:
self.cuda.set_device(int(devid))
# Take the required alignment to be 128 bytes
self.alignb = 128
# Take the SoA size to be 32 elements
self.soasz = 32
self.csubsz = self.soasz
# Get the MPI runtime type
self.mpitype = cfg.get('backend-cuda', 'mpi-type', 'standard')
if self.mpitype not in {'standard', 'cuda-aware'}:
raise ValueError('Invalid CUDA backend MPI type')
# Some CUDA devices share L1 cache and shared memory; on these
# devices CUDA allows us to specify a preference between L1
# cache and shared memory. For the sake of CUBLAS (which
# benefits greatly from more shared memory but fails to
# declare its preference) we set the global default to
# PREFER_SHARED.
self.cuda.set_cache_pref(prefer_shared=True)
from pyfr.backends.cuda import (blasext, cublas, gimmik, packing,
provider, types)
# Register our data types
self.base_matrix_cls = types.CUDAMatrixBase
self.const_matrix_cls = types.CUDAConstMatrix
self.matrix_cls = types.CUDAMatrix
self.matrix_bank_cls = types.CUDAMatrixBank
self.matrix_slice_cls = types.CUDAMatrixSlice
self.queue_cls = types.CUDAQueue
self.view_cls = types.CUDAView
self.xchg_matrix_cls = types.CUDAXchgMatrix
self.xchg_view_cls = types.CUDAXchgView
# Instantiate the base kernel providers
kprovs = [provider.CUDAPointwiseKernelProvider,
blasext.CUDABlasExtKernels,
packing.CUDAPackingKernels,
gimmik.CUDAGiMMiKKernels,
cublas.CUDACUBLASKernels]
self._providers = [k(self) for k in kprovs]
# Pointwise kernels
self.pointwise = self._providers[0]
[docs] def _malloc_impl(self, nbytes):
# Allocate
data = self.cuda.mem_alloc(nbytes)
# Zero
self.cuda.memset(data, 0, nbytes)
return data