def __init__(self, exprs):
"""
A Scope represents a group of :class:`TimedAccess` objects extracted
from some :class:`IREq` ``exprs``. The expressions must be provided
in program order.
"""
exprs = as_tuple(exprs)
self.reads = {}
self.writes = {}
for i, e in enumerate(exprs):
# reads
for j in retrieve_terminals(e.rhs):
v = self.reads.setdefault(j.base.function, [])
mode = 'R' if not q_inc(e) else 'RI'
v.append(TimedAccess(j, mode, i, e.ispace.directions))
# write
v = self.writes.setdefault(e.lhs.base.function, [])
mode = 'W' if not q_inc(e) else 'WI'
v.append(TimedAccess(e.lhs, mode, i, e.ispace.directions))
def __init__(self, exprs):
"""
Initialize a Scope, which represents a group of :class:`Access` objects
extracted from some expressions ``exprs``. The expressions are to be
provided as they appear in program order.
"""
exprs = as_tuple(exprs)
assert all(isinstance(i, Eq) for i in exprs)
self.reads = {}
self.writes = {}
for i, e in enumerate(exprs):
# reads
for j in retrieve_terminals(e.rhs):
v = self.reads.setdefault(j.base.function, [])
mode = 'R' if not q_inc(e) else 'RI'
v.append(TimedAccess(j, mode, i))
# write
v = self.writes.setdefault(e.lhs.base.function, [])
mode = 'W' if not q_inc(e) else 'WI'
v.append(TimedAccess(e.lhs, mode, i))
def __init__(self, exprs, **kwargs):
# Check input legality
mapper = OrderedDict([(i.lhs, i) for i in exprs])
if len(set(mapper)) != len(mapper):
raise DSEException(
"Found redundant node, cannot build TemporariesGraph.")
# Construct Temporaries, tracking reads and readby
tensor_map = DefaultOrderedDict(list)
for i in mapper:
tensor_map[as_symbol(i)].append(i)
reads = DefaultOrderedDict(set)
readby = DefaultOrderedDict(set)
for k, v in mapper.items():
handle = retrieve_terminals(v.rhs)
for i in list(handle):
if i.is_Indexed:
for idx in i.indices:
handle |= retrieve_terminals(idx)
reads[k].update(
set(flatten([tensor_map.get(as_symbol(i), [])
for i in handle])))
for i in reads[k]:
readby[i].add(k)
# Make sure read-after-writes are honored for scalar temporaries
processed = [i for i in mapper if i.is_Indexed]
queue = [i for i in mapper if i not in processed]
while queue:
k = queue.pop(0)
if not readby[k]:
processed.insert(0, k)
elif all(i in processed for i in readby[k]):
index = min(processed.index(i) for i in readby[k])
processed.insert(index, k)
else:
queue.append(k)
# Build up the TemporariesGraph
temporaries = [(i,
Temporary(*mapper[i].args,
inc=q_inc(mapper[i]),
reads=reads[i],
readby=readby[i])) for i in processed]
super(TemporariesGraph, self).__init__(temporaries, **kwargs)
# Determine indices along the space and time dimensions
terms = [
v for k, v in self.items() if v.is_tensor and not q_indirect(k)
]
indices = filter_ordered(flatten([i.function.indices for i in terms]))
self.space_indices = tuple(i for i in indices if i.is_Space)
self.time_indices = tuple(i for i in indices if i.is_Time)
def __init__(self, exprs, **kwargs):
# Always convert to SSA
exprs = convert_to_SSA(exprs)
mapper = OrderedDict([(i.lhs, i) for i in exprs])
assert len(set(mapper)) == len(exprs), "not SSA Cluster?"
# Construct the Nodes, tracking reads and readby
tensor_map = DefaultOrderedDict(list)
for i in mapper:
tensor_map[as_symbol(i)].append(i)
reads = DefaultOrderedDict(set)
readby = DefaultOrderedDict(set)
for k, v in mapper.items():
handle = retrieve_terminals(v.rhs)
for i in list(handle):
if i.is_Indexed:
for idx in i.indices:
handle |= retrieve_terminals(idx)
reads[k].update(
set(flatten([tensor_map.get(as_symbol(i), [])
for i in handle])))
for i in reads[k]:
readby[i].add(k)
# Make sure read-after-writes are honored for scalar temporaries
processed = [i for i in mapper if i.is_Indexed]
queue = [i for i in mapper if i not in processed]
while queue:
k = queue.pop(0)
if not readby[k]:
processed.insert(0, k)
elif all(i in processed for i in readby[k]):
index = min(processed.index(i) for i in readby[k])
processed.insert(index, k)
else:
queue.append(k)
# Build up the FlowGraph
temporaries = [(i,
Node(*mapper[i].args,
inc=q_inc(mapper[i]),
reads=reads[i],
readby=readby[i])) for i in processed]
super(FlowGraph, self).__init__(temporaries, **kwargs)
# Determine indices along the space and time dimensions
terms = [
v for k, v in self.items() if v.is_tensor and not q_indirect(k)
]
indices = filter_ordered(flatten([i.function.indices for i in terms]))
self.space_indices = tuple(i for i in indices if i.is_Space)
self.time_indices = tuple(i for i in indices if i.is_Time)