def __init__(self, cfg):
super(CUDABackend, self).__init__(cfg)
# Get the desired CUDA device
devid = cfg.get('backend-cuda', 'device-id', 'round-robin')
if not re.match(r'(round-robin|local-rank|\d+)$', devid):
raise ValueError('Invalid device-id')
# Handle the local-rank case
if devid == 'local-rank':
devid = str(get_local_rank())
# In the non round-robin case set CUDA_DEVICE to be the desired
# CUDA device number (used by pycuda.autoinit)
os.environ.pop('CUDA_DEVICE', None)
if devid != 'round-robin':
os.environ['CUDA_DEVICE'] = devid
# Create a CUDA context
from pycuda.autoinit import context
import pycuda.driver as cuda
# Take the required alignment to be 128 bytes
self.alignb = 128
# Some CUDA devices share L1 cache and shared memory; on these
# devices CUDA allows us to specify a preference between L1
# cache and shared memory. For the sake of CUBLAS (which
# benefits greatly from more shared memory but fails to
# declare its preference) we set the global default to
# PREFER_SHARED.
context.set_cache_config(cuda.func_cache.PREFER_SHARED)
from pyfr.backends.cuda import (blasext, cublas, packing, provider,
types)
# Register our data types
self.base_matrix_cls = types.CUDAMatrixBase
self.const_matrix_cls = types.CUDAConstMatrix
self.matrix_cls = types.CUDAMatrix
self.matrix_bank_cls = types.CUDAMatrixBank
self.matrix_rslice_cls = types.CUDAMatrixRSlice
self.mpi_matrix_cls = types.CUDAMPIMatrix
self.mpi_view_cls = types.CUDAMPIView
self.queue_cls = types.CUDAQueue
self.view_cls = types.CUDAView
# Template lookup
self.lookup = DottedTemplateLookup('pyfr.backends.cuda.kernels')
# Instantiate the base kernel providers
kprovs = [
provider.CUDAPointwiseKernelProvider, blasext.CUDABlasExtKernels,
packing.CUDAPackingKernels, cublas.CUDACUBLASKernels
]
self._providers = [k(self) for k in kprovs]
# Pointwise kernels
self.pointwise = self._providers[0]
def __init__(self, cfg):
super(CUDABackend, self).__init__(cfg)
# Get the desired CUDA device
devid = cfg.get('backend-cuda', 'device-id', 'round-robin')
if not re.match(r'(round-robin|local-rank|\d+)$', devid):
raise ValueError('Invalid device-id')
# Handle the local-rank case
if devid == 'local-rank':
devid = str(get_local_rank())
# In the non round-robin case set CUDA_DEVICE to be the desired
# CUDA device number (used by pycuda.autoinit)
os.environ.pop('CUDA_DEVICE', None)
if devid != 'round-robin':
os.environ['CUDA_DEVICE'] = devid
# Create a CUDA context
from pycuda.autoinit import context
import pycuda.driver as cuda
# Take the required alignment to be 128 bytes
self.alignb = 128
# 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.
context.set_cache_config(cuda.func_cache.PREFER_SHARED)
from pyfr.backends.cuda import (blasext, cublas, 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_rslice_cls = types.CUDAMatrixRSlice
self.mpi_matrix_cls = types.CUDAMPIMatrix
self.mpi_view_cls = types.CUDAMPIView
self.queue_cls = types.CUDAQueue
self.view_cls = types.CUDAView
# Template lookup
self.lookup = DottedTemplateLookup('pyfr.backends.cuda.kernels')
# Instantiate the base kernel providers
kprovs = [provider.CUDAPointwiseKernelProvider,
blasext.CUDABlasExtKernels,
packing.CUDAPackingKernels,
cublas.CUDACUBLASKernels]
self._providers = [k(self) for k in kprovs]
# Pointwise kernels
self.pointwise = self._providers[0]
def __init__(self, cfg):
super(CUDABackend, self).__init__(cfg)
# Create a CUDA context
from pycuda.autoinit import context as cuda_ctx
# 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.
from pycuda.driver import func_cache
cuda_ctx.set_cache_config(func_cache.PREFER_SHARED)
# For introspection to work it must always be possible to
# import the CUDABackend (even if CUDA is unavailable on the
# system). As many of our types/providers depend on the CUDA
# runtime we import these here, locally, at the time of
# instantiation.
from pyfr.backends.cuda import (blasext, cublas, packing, provider,
types)
# Register our data types
self.block_diag_matrix_cls = types.CUDABlockDiagMatrix
self.const_matrix_cls = types.CUDAConstMatrix
self.matrix_cls = types.CUDAMatrix
self.matrix_bank_cls = types.CUDAMatrixBank
self.matrix_rslice_cls = types.CUDAMatrixRSlice
self.mpi_matrix_cls = types.CUDAMPIMatrix
self.mpi_view_cls = types.CUDAMPIView
self.queue_cls = types.CUDAQueue
self.view_cls = types.CUDAView
# Template lookup
self.lookup = DottedTemplateLookup('pyfr.backends.cuda.kernels')
# Instantiate the base kernel providers
kprovs = [provider.CUDAPointwiseKernelProvider,
blockmats.BlockDiagMatrixKernels,
blasext.CUDABlasExtKernels,
packing.CUDAPackingKernels,
cublas.CUDACublasKernels]
self._providers = [k(self) for k in kprovs]
# Pointwise kernels
self.pointwise = self._providers[0]
def __init__(self, cfg):
super(CUDABackend, self).__init__(cfg)
# Create a CUDA context
from pycuda.autoinit import context
# Take the required alignment to be 128 bytes
self.alignb = 128
# 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.
from pycuda.driver import func_cache
context.set_cache_config(func_cache.PREFER_SHARED)
from pyfr.backends.cuda import (blasext, cublas, packing, provider,
types)
# Register our data types
self.const_matrix_cls = types.CUDAConstMatrix
self.matrix_cls = types.CUDAMatrix
self.matrix_bank_cls = types.CUDAMatrixBank
self.matrix_rslice_cls = types.CUDAMatrixRSlice
self.mpi_matrix_cls = types.CUDAMPIMatrix
self.mpi_view_cls = types.CUDAMPIView
self.queue_cls = types.CUDAQueue
self.view_cls = types.CUDAView
# Template lookup
self.lookup = DottedTemplateLookup('pyfr.backends.cuda.kernels')
# Instantiate the base kernel providers
kprovs = [provider.CUDAPointwiseKernelProvider,
blasext.CUDABlasExtKernels,
packing.CUDAPackingKernels,
cublas.CUDACUBLASKernels]
self._providers = [k(self) for k in kprovs]
# Pointwise kernels
self.pointwise = self._providers[0]
def __init__(self, cfg):
super().__init__(cfg)
# Get the desired CUDA device
devid = cfg.get('backend-cuda', 'device-id', 'local-rank')
if not re.match(r'(round-robin|local-rank|\d+)$', devid):
raise ValueError('Invalid device-id')
# In the non round-robin case set CUDA_DEVICE to be the desired
# CUDA device number (used by pycuda.autoinit)
os.environ.pop('CUDA_DEVICE', None)
# Handle the local-rank case
if devid == 'local-rank':
devord = str(get_local_rank())
os.environ['CUDA_DEVICE'] = devord
# Create a CUDA context
from pycuda.autoinit import context
import pycuda.driver as cuda
# Aforementioned commented lines do not work for multiple gpus/node
"""
if devid == 'local-rank':
import pycuda.driver as cuda
cuda.init()
cudadevice = cuda.Device(int(devord))
context = cudadevice.make_context()
#import atexit
#atexit.register(context.pop)
elif devid == 'round-robin':
from pycuda.autoinit import context
import pycuda.driver as cuda
"""
# Take the required alignment to be 128 bytes
self.alignb = 128
# Take the SoA size to be 32 elements
self.soasz = 32
# 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.
context.set_cache_config(cuda.func_cache.PREFER_SHARED)
#self.context = context
from frfs.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_rslice_cls = types.CUDAMatrixRSlice
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]
pred_obs_mean_ar[tid], &fltr_stt_mean_ar[offset_mean+wid], dim_state_ar, tid, i, iteration);
fltr_stt_cov_ar_cal(&pred_stt_cov_ar[offset_cov+wid],
kalman_gain_ar,
&fltr_stt_cov_ar[offset_cov+wid], dim_state_ar, temp, temp1, tid);
}
logpdf[tid] = log_pdf(pred_obs_mean_ar[tid], pred_obs_cov_ar[tid],tid);
}
""")
start = time.time()
context.set_cache_config(cuda.func_cache.PREFER_L1)
filter = mod.get_function("filter")
init_stt_mean_ar = mod.get_global('init_stt_mean_ar')[0]
init_stt_cov_ar = mod.get_global('init_stt_cov_ar')[0]
tran_mat_ar = mod.get_global('tran_mat_ar')[0]
observations_ar = mod.get_global('observations_ar')[0]
cuda.memcpy_htod(init_stt_mean_ar, init_stt_mean_ar_const)
cuda.memcpy_htod(init_stt_cov_ar, init_stt_cov_ar_const)
cuda.memcpy_htod(tran_mat_ar, tran_mat_ar_const)
cuda.memcpy_htod(observations_ar, observations_ar_const)
start = time.time()
