def _specialize_iet(cls, graph, **kwargs):
options = kwargs['options']
platform = kwargs['platform']
sregistry = kwargs['sregistry']
# Flush denormal numbers
avoid_denormals(graph)
# Distributed-memory parallelism
optimize_halospots(graph)
if options['mpi']:
mpiize(graph, mode=options['mpi'])
# Lower IncrDimensions so that blocks of arbitrary shape may be used
relax_incr_dimensions(graph, sregistry=sregistry)
# Parallelism
parizer = cls._Target.Parizer(sregistry, options, platform)
parizer.make_simd(graph)
parizer.make_parallel(graph)
# Misc optimizations
hoist_prodders(graph)
# Symbol definitions
cls._Target.DataManager(sregistry).process(graph)
# Initialize the target-language runtime
parizer.initialize(graph)
return graph
def _specialize_iet(cls, graph, **kwargs):
options = kwargs['options']
platform = kwargs['platform']
# Flush denormal numbers
avoid_denormals(graph)
# Distributed-memory parallelism
optimize_halospots(graph)
if options['mpi']:
mpiize(graph, mode=options['mpi'])
# Lower IncrDimensions so that blocks of arbitrary shape may be used
relax_incr_dimensions(graph, counter=generator())
# SIMD-level parallelism
ompizer = Ompizer()
ompizer.make_simd(graph, simd_reg_size=platform.simd_reg_size)
# Shared-memory parallelism
ompizer.make_parallel(graph)
# Misc optimizations
hoist_prodders(graph)
# Symbol definitions
data_manager = DataManager()
data_manager.place_definitions(graph)
data_manager.place_casts(graph)
return graph
def _specialize_iet(cls, graph, **kwargs):
options = kwargs['options']
platform = kwargs['platform']
compiler = kwargs['compiler']
sregistry = kwargs['sregistry']
# Flush denormal numbers
avoid_denormals(graph, platform=platform)
# Distributed-memory parallelism
mpiize(graph, sregistry=sregistry, options=options)
# Lower BlockDimensions so that blocks of arbitrary shape may be used
relax_incr_dimensions(graph)
# Parallelism
parizer = cls._Target.Parizer(sregistry, options, platform, compiler)
parizer.make_simd(graph)
parizer.make_parallel(graph)
parizer.initialize(graph)
# Misc optimizations
hoist_prodders(graph)
# Symbol definitions
cls._Target.DataManager(sregistry).process(graph)
# Linearize n-dimensional Indexeds
linearize(graph, mode=options['linearize'], sregistry=sregistry)
return graph
def _specialize_iet(cls, graph, **kwargs):
options = kwargs['options']
platform = kwargs['platform']
sregistry = kwargs['sregistry']
# Distributed-memory parallelism
optimize_halospots(graph)
if options['mpi']:
mpiize(graph, mode=options['mpi'])
# Lower IncrDimensions so that blocks of arbitrary shape may be used
relax_incr_dimensions(graph, sregistry=sregistry)
# SIMD-level parallelism
ompizer = Ompizer(sregistry, options)
ompizer.make_simd(graph, simd_reg_size=platform.simd_reg_size)
# Shared-memory parallelism
ompizer.make_parallel(graph)
# Misc optimizations
hoist_prodders(graph)
# Symbol definitions
DataManager(sregistry).process(graph)
return graph
def _specialize_iet(cls, graph, **kwargs):
options = kwargs['options']
platform = kwargs['platform']
compiler = kwargs['compiler']
sregistry = kwargs['sregistry']
# Distributed-memory parallelism
mpiize(graph, sregistry=sregistry, options=options)
# Loop tiling
relax_incr_dimensions(graph)
# GPU parallelism
parizer = cls._Target.Parizer(sregistry, options, platform, compiler)
parizer.make_parallel(graph)
parizer.initialize(graph)
# Misc optimizations
hoist_prodders(graph)
# Symbol definitions
cls._Target.DataManager(sregistry, options).process(graph)
# Linearize n-dimensional Indexeds
linearize(graph, mode=options['linearize'], sregistry=sregistry)
return graph
