def _make_partree(self, candidates, omp_pragma):
"""Parallelize `root` attaching a suitable OpenMP pragma."""
assert candidates
root = candidates[0]
# Get the collapsable Iterations
collapsable = []
if ncores() >= Ompizer.COLLAPSE and IsPerfectIteration().visit(root):
for n, i in enumerate(candidates[1:], 1):
# The OpenMP specification forbids collapsed loops to use iteration
# variables in initializer expressions. E.g., the following is forbidden:
#
# #pragma omp ... collapse(2)
# for (i = ... )
# for (j = i ...)
# ...
#
# Here, we make sure this won't happen
if any(j.dim in i.symbolic_min.free_symbols for j in candidates[:n]):
break
# Also, we do not want to collapse vectorizable Iterations
if i.is_Vectorizable:
break
collapsable.append(i)
# Attach an OpenMP pragma-for with a collapse clause
ncollapse = 1 + len(collapsable)
partree = root._rebuild(pragmas=root.pragmas + (omp_pragma(ncollapse),),
properties=root.properties + (COLLAPSED(ncollapse),))
collapsed = [partree] + collapsable
return root, partree, collapsed
def _make_partree(self, candidates, omp_pragma=None):
"""Parallelize `root` attaching a suitable OpenMP pragma."""
assert candidates
root = candidates[0]
# Pick up an omp-pragma template
# Caller-provided -> stick to it
# Affine -> ... schedule(static,1) ...
# Non-affine -> ... schedule(static) ...
if omp_pragma is None:
if all(i.is_Affine for i in candidates):
omp_pragma = self.lang['for-static-1']
else:
omp_pragma = self.lang['for-static']
# Get the collapsable Iterations
collapsable = []
if ncores() >= Ompizer.COLLAPSE_NCORES and IsPerfectIteration().visit(
root):
for n, i in enumerate(candidates[1:], 1):
# The OpenMP specification forbids collapsed loops to use iteration
# variables in initializer expressions. E.g., the following is forbidden:
#
# #pragma omp ... collapse(2)
# for (i = ... )
# for (j = i ...)
# ...
#
# Here, we make sure this won't happen
if any(j.dim in i.symbolic_min.free_symbols
for j in candidates[:n]):
break
# Also, we do not want to collapse vectorizable Iterations
if i.is_Vectorizable:
break
# Would there be enough work per parallel iteration?
try:
work = prod(
[int(j.dim.symbolic_size) for j in candidates[n + 1:]])
if work < Ompizer.COLLAPSE_WORK:
break
except TypeError:
pass
collapsable.append(i)
# Attach an OpenMP pragma-for with a collapse clause
ncollapse = 1 + len(collapsable)
partree = root._rebuild(
pragmas=root.pragmas + (omp_pragma(ncollapse), ),
properties=root.properties + (COLLAPSED(ncollapse), ))
collapsed = [partree] + collapsable
return root, partree, collapsed
def __init__(self, *args, **kwargs):
kwargs.pop('pragmas', None)
pragma = self._make_header(**kwargs)
properties = as_tuple(kwargs.pop('properties', None))
properties += (COLLAPSED(kwargs.get('ncollapse', 1)),)
self.parallel = kwargs.pop('parallel', False)
self.ncollapse = kwargs.pop('ncollapse', None)
self.chunk_size = kwargs.pop('chunk_size', None)
self.nthreads = kwargs.pop('nthreads', None)
self.reduction = kwargs.pop('reduction', None)
super(ParallelIteration, self).__init__(*args, pragmas=[pragma],
properties=properties, **kwargs)
def _make_partree(self, candidates, nthreads=None):
"""Parallelize the `candidates` Iterations attaching suitable OpenMP pragmas."""
assert candidates
root = candidates[0]
# Get the collapsable Iterations
collapsable = self._find_collapsable(root, candidates)
ncollapse = 1 + len(collapsable)
# Prepare to build a ParallelTree
prefix = []
if all(i.is_Affine for i in candidates):
if nthreads is None:
# pragma omp for ... schedule(..., 1)
nthreads = self.nthreads
omp_pragma = self.lang['for'](ncollapse, 1)
else:
# pragma omp parallel for ... schedule(..., 1)
omp_pragma = self.lang['par-for'](ncollapse, 1, nthreads)
else:
# pragma omp for ... schedule(..., expr)
assert nthreads is None
nthreads = self.nthreads_nonaffine
chunk_size = Symbol(name='chunk_size')
omp_pragma = self.lang['for'](ncollapse, chunk_size)
niters = prod([root.symbolic_size] +
[j.symbolic_size for j in collapsable])
value = INT(Max(niters / (nthreads * self.CHUNKSIZE_NONAFFINE), 1))
prefix.append(
Expression(DummyEq(chunk_size, value, dtype=np.int32)))
# Create a ParallelTree
body = root._rebuild(pragmas=root.pragmas + (omp_pragma, ),
properties=root.properties +
(COLLAPSED(ncollapse), ))
partree = ParallelTree(prefix, body, nthreads=nthreads)
collapsed = [partree] + collapsable
return root, partree, collapsed
def _make_partree(self, candidates, nthreads=None):
"""Parallelize `root` attaching a suitable OpenMP pragma."""
assert candidates
root = candidates[0]
# Get the collapsable Iterations
collapsable = []
if ncores() >= Ompizer.COLLAPSE_NCORES and IsPerfectIteration().visit(
root):
for n, i in enumerate(candidates[1:], 1):
# The OpenMP specification forbids collapsed loops to use iteration
# variables in initializer expressions. E.g., the following is forbidden:
#
# #pragma omp ... collapse(2)
# for (i = ... )
# for (j = i ...)
# ...
#
# Here, we make sure this won't happen
if any(j.dim in i.symbolic_min.free_symbols
for j in candidates[:n]):
break
# Also, we do not want to collapse vectorizable Iterations
if i.is_Vectorizable:
break
# Would there be enough work per parallel iteration?
try:
work = prod(
[int(j.dim.symbolic_size) for j in candidates[n + 1:]])
if work < Ompizer.COLLAPSE_WORK:
break
except TypeError:
pass
collapsable.append(i)
ncollapse = 1 + len(collapsable)
# Prepare to build a ParallelTree
prefix = []
if all(i.is_Affine for i in candidates):
if nthreads is None:
# pragma omp for ... schedule(..., 1)
nthreads = self.nthreads
omp_pragma = self.lang['for'](ncollapse, 1)
else:
# pragma omp parallel for ... schedule(..., 1)
omp_pragma = self.lang['par-for'](ncollapse, 1, nthreads)
else:
# pragma omp for ... schedule(..., expr)
assert nthreads is None
nthreads = self.nthreads_nonaffine
chunk_size = Symbol(name='chunk_size')
omp_pragma = self.lang['for'](ncollapse, chunk_size)
niters = prod([root.symbolic_size] +
[j.symbolic_size for j in collapsable])
value = INT(Max(niters / (nthreads * self.CHUNKSIZE_NONAFFINE), 1))
prefix.append(
Expression(DummyEq(chunk_size, value, dtype=np.int32)))
# Create a ParallelTree
body = root._rebuild(pragmas=root.pragmas + (omp_pragma, ),
properties=root.properties +
(COLLAPSED(ncollapse), ))
partree = ParallelTree(prefix, body, nthreads=nthreads)
collapsed = [partree] + collapsable
return root, partree, collapsed
