def make_parallel(self, iet):
"""
Transform ``iet`` by decorating its parallel :class:`Iteration`s with
suitable ``#pragma omp ...`` for thread-level parallelism.
"""
# Group sequences of loops that should go within the same parallel region
was_tagged = False
groups = OrderedDict()
for tree in retrieve_iteration_tree(iet):
# Determine the number of consecutive parallelizable Iterations
candidates = filter_iterations(tree, key=self.key, stop='asap')
if not candidates:
was_tagged = False
continue
# Consecutive tagged Iteration go in the same group
is_tagged = any(i.tag is not None for i in tree)
key = len(groups) - (is_tagged & was_tagged)
handle = groups.setdefault(key, OrderedDict())
handle[candidates[0]] = candidates
was_tagged = is_tagged
mapper = OrderedDict()
for group in groups.values():
private = []
for root, candidates in group.items():
mapper.update(self._make_parallel_tree(root, candidates))
# Track the thread-private and thread-shared variables
private.extend([
i for i in FindSymbols('symbolics').visit(root)
if i.is_Array and i._mem_stack
])
# Build the parallel region
private = sorted(set([i.name for i in private]))
private = ('private(%s)' % ','.join(private)) if private else ''
rebuilt = [v for k, v in mapper.items() if k in group]
par_region = Block(header=self.lang['par-region'](private),
body=rebuilt)
for k, v in list(mapper.items()):
if isinstance(v, Iteration):
mapper[k] = None if v.is_Remainder else par_region
processed = Transformer(mapper).visit(iet)
# Hack/workaround to the fact that the OpenMP pragmas are not true
# IET nodes, so the `nthreads` variables won't be detected as a
# Callable parameter unless inserted in a mock Expression
if mapper:
nt = NThreads()
eq = LocalExpression(DummyEq(Symbol(name='nt', dtype=np.int32),
nt))
return List(body=[eq, processed]), {'input': [nt]}
else:
return List(body=processed), {}
def mpi_gpu_direct(iet, **kwargs):
"""
Modify MPI Callables to enable multiple GPUs performing GPU-Direct communication.
"""
mapper = {}
for node in FindNodes((IsendCall, IrecvCall)).visit(iet):
header = c.Pragma('omp target data use_device_ptr(%s)' %
node.arguments[0].name)
mapper[node] = Block(header=header, body=node)
iet = Transformer(mapper).visit(iet)
return iet, {}
def test_transformer_replace(exprs, block1, block2, block3):
"""Basic transformer test that replaces an expression"""
line1 = '// Replaced expression'
replacer = Block(c.Line(line1))
transformer = Transformer({exprs[0]: replacer})
for block in [block1, block2, block3]:
newblock = transformer.visit(block)
newcode = str(newblock.ccode)
oldnumlines = len(str(block.ccode).split('\n'))
newnumlines = len(newcode.split('\n'))
assert newnumlines >= oldnumlines
assert line1 in newcode
assert "a[i0] = a[i0] + b[i0] + 5.0F;" not in newcode
def test_transformer_wrap(exprs, block1, block2, block3):
"""Basic transformer test that wraps an expression in comments"""
line1 = '// This is the opening comment'
line2 = '// This is the closing comment'
wrapper = lambda n: Block(c.Line(line1), n, c.Line(line2))
transformer = Transformer({exprs[0]: wrapper(exprs[0])})
for block in [block1, block2, block3]:
newblock = transformer.visit(block)
newcode = str(newblock.ccode)
oldnumlines = len(str(block.ccode).split('\n'))
newnumlines = len(newcode.split('\n'))
assert newnumlines >= oldnumlines + 2
assert line1 in newcode
assert line2 in newcode
assert "a[i] = a[i] + b[i] + 5.0F;" in newcode
def make_parallel(self, iet):
"""
Transform ``iet`` by decorating its parallel :class:`Iteration`s with
suitable ``#pragma omp ...`` triggering thread-level parallelism.
"""
# Group sequences of loops that should go within the same parallel region
was_tagged = False
groups = OrderedDict()
for tree in retrieve_iteration_tree(iet):
# Determine the number of consecutive parallelizable Iterations
candidates = filter_iterations(tree, key=self.key, stop='asap')
if not candidates:
was_tagged = False
continue
# Consecutive tagged Iteration go in the same group
is_tagged = any(i.tag is not None for i in tree)
key = len(groups) - (is_tagged & was_tagged)
handle = groups.setdefault(key, OrderedDict())
handle[candidates[0]] = candidates
was_tagged = is_tagged
mapper = OrderedDict()
for group in groups.values():
private = []
for root, candidates in group.items():
mapper.update(self._make_parallel_tree(root, candidates))
# Track the thread-private and thread-shared variables
private.extend([i for i in FindSymbols('symbolics').visit(root)
if i.is_Array and i._mem_stack])
# Build the parallel region
private = sorted(set([i.name for i in private]))
private = ('private(%s)' % ','.join(private)) if private else ''
rebuilt = [v for k, v in mapper.items() if k in group]
par_region = Block(header=self.lang['par-region'](private), body=rebuilt)
for k, v in list(mapper.items()):
if isinstance(v, Iteration):
mapper[k] = None if v.is_Remainder else par_region
return Transformer(mapper).visit(iet)
def make_parallel(self, iet):
"""Transform ``iet`` by introducing shared-memory parallelism."""
mapper = OrderedDict()
for tree in retrieve_iteration_tree(iet):
# Get the first omp-parallelizable Iteration in `tree`
candidates = filter_iterations(tree, key=self.key, stop='asap')
if not candidates:
continue
root = candidates[0]
# Build the `omp-for` tree
partree = self._make_parallel_tree(root, candidates)
# Find out the thread-private and thread-shared variables
private = [
i for i in FindSymbols().visit(partree)
if i.is_Array and i._mem_stack
]
# Build the `omp-parallel` region
private = sorted(set([i.name for i in private]))
private = ('private(%s)' % ','.join(private)) if private else ''
partree = Block(header=self.lang['par-region'](self.nthreads.name,
private),
body=partree)
# Do not enter the parallel region if the step increment might be 0; this
# would raise a `Floating point exception (core dumped)` in some OpenMP
# implementation. Note that using an OpenMP `if` clause won't work
if isinstance(root.step, Symbol):
cond = Conditional(CondEq(root.step, 0),
Element(c.Statement('return')))
partree = List(body=[cond, partree])
mapper[root] = partree
iet = Transformer(mapper).visit(iet)
return iet, {'input': [self.nthreads] if mapper else []}
