def iet_build(stree):
"""
Create an Iteration/Expression tree (IET) from a :class:`ScheduleTree`.
The nodes in the returned IET are decorated with properties deriving from
data dependence analysis.
"""
# Schedule tree -> Iteration/Expression tree
iet = iet_make(stree)
# Data dependency analysis. Properties are attached directly to nodes
iet = iet_analyze(iet)
# Replace stepping dimensions with modulo dimensions
iet = iet_lower_steppers(iet)
return iet
def iet_build(stree):
"""
Create an Iteration/Expression tree (IET) from a :class:`ScheduleTree`.
The nodes in the returned IET are decorated with properties deriving from
data dependence analysis.
"""
# Schedule tree -> Iteration/Expression tree
iet = iet_make(stree)
# Data dependency analysis. Properties are attached directly to nodes
iet = iet_analyze(iet)
# Turn DerivedDimensions into lower-level Dimensions or Symbols
iet = iet_lower_dimensions(iet)
return iet
def iet_build(stree):
"""
Create an Iteration/Expression tree (IET) from a ScheduleTree.
The nodes in the returned IET are decorated with properties deriving from
data dependence analysis.
"""
# Schedule tree -> Iteration/Expression tree
iet = iet_make(stree)
# Data dependency analysis. Properties are attached directly to nodes
iet = iet_analyze(iet)
# Turn DerivedDimensions into lower-level Dimensions or Symbols
iet = iet_lower_dimensions(iet)
return iet
def test_loops_collapsed(fe, t0, t1, t2, t3, exprs, expected, iters):
scope = [fe, t0, t1, t2, t3]
node_exprs = [Expression(DummyEq(EVAL(i, *scope))) for i in exprs]
ast = iters[6](iters[7](iters[8](node_exprs)))
ast = iet_analyze(ast)
nodes = transform(ast, mode='openmp').nodes
iterations = FindNodes(Iteration).visit(nodes)
assert len(iterations) == len(expected)
# Check for presence of pragma omp
for i, j in zip(iterations, expected):
pragmas = i.pragmas
if j is True:
assert len(pragmas) == 1
pragma = pragmas[0]
assert 'omp for collapse' in pragma.value
else:
for k in pragmas:
assert 'omp for collapse' not in k.value
def test_iterations_ompized(self, fa, fb, fc, fd, t0, t1, t2, t3,
exprs, expected, iters):
scope = [fa, fb, fc, fd, t0, t1, t2, t3]
node_exprs = [Expression(DummyEq(EVAL(i, *scope))) for i in exprs]
ast = iters[6](iters[7](node_exprs))
ast = iet_analyze(ast)
iet, _ = transform(ast, mode='openmp')
iterations = FindNodes(Iteration).visit(iet)
assert len(iterations) == len(expected)
# Check for presence of pragma omp
for i, j in zip(iterations, expected):
pragmas = i.pragmas
if j is True:
assert len(pragmas) == 1
pragma = pragmas[0]
assert 'omp for' in pragma.value
else:
for k in pragmas:
assert 'omp for' not in k.value
def test_iterations_ompized(self, fa, fb, fc, fd, t0, t1, t2, t3, exprs,
expected, iters):
scope = [fa, fb, fc, fd, t0, t1, t2, t3]
node_exprs = [Expression(DummyEq(EVAL(i, *scope))) for i in exprs]
ast = iters[6](iters[7](node_exprs))
ast = iet_analyze(ast)
iet, _ = transform(ast, mode='openmp')
iterations = FindNodes(Iteration).visit(iet)
assert len(iterations) == len(expected)
# Check for presence of pragma omp
for i, j in zip(iterations, expected):
pragmas = i.pragmas
if j is True:
assert len(pragmas) == 1
pragma = pragmas[0]
assert 'omp for' in pragma.value
else:
for k in pragmas:
assert 'omp for' not in k.value
def iet_build(clusters, dtype):
"""
Create an Iteration/Expression tree (IET) given an iterable of :class:`Cluster`s.
The nodes in the returned IET are decorated with properties deriving from
data dependence analysis.
"""
# Clusters -> Iteration/Expression tree
iet = iet_make(clusters, dtype)
# Data dependency analysis. Properties are attached directly to nodes
iet = iet_analyze(iet)
# Substitute derived dimensions (e.g., t -> t0, t + 1 -> t1)
# This is postponed up to this point to ease /iet_analyze/'s life
subs = {}
for tree in retrieve_iteration_tree(iet):
uindices = flatten(i.uindices for i in tree)
subs.update({i.expr: LoweredDimension(name=i.index.name, origin=i.expr)
for i in uindices})
iet = SubstituteExpression(subs).visit(iet)
return iet
