def _aliases_from_clusters(self, clusters, exclude, meta):
exprs = flatten([c.exprs for c in clusters])
# [Clusters]_n -> [Schedule]_m
variants = []
for mapper in self._generate(exprs, exclude):
# Clusters -> AliasList
found = collect(mapper.extracted, meta.ispace, self.opt_minstorage)
pexprs, aliases = choose(found, exprs, mapper, self.opt_mingain)
if not aliases:
continue
# AliasList -> Schedule
schedule = lower_aliases(aliases, meta, self.opt_maxpar)
variants.append(Variant(schedule, pexprs))
# [Schedule]_m -> Schedule (s.t. best memory/flops trade-off)
if not variants:
return []
schedule, exprs = pick_best(variants, self.opt_schedule_strategy,
self._eval_variants_delta)
# Schedule -> Schedule (optimization)
if self.opt_rotate:
schedule = optimize_schedule_rotations(schedule, self.sregistry)
schedule = optimize_schedule_padding(schedule, meta, self.platform)
# Schedule -> [Clusters]_k
processed, subs = lower_schedule(schedule, meta, self.sregistry,
self.opt_ftemps)
# [Clusters]_k -> [Clusters]_k (optimization)
if self.opt_multisubdomain:
processed = optimize_clusters_msds(processed)
# [Clusters]_k -> [Clusters]_{k+n}
for c in clusters:
n = len(c.exprs)
cexprs, exprs = exprs[:n], exprs[n:]
cexprs = [uxreplace(e, subs) for e in cexprs]
ispace = c.ispace.augment(schedule.dmapper)
ispace = ispace.augment(schedule.rmapper)
accesses = detect_accesses(cexprs)
parts = {
k: IntervalGroup(build_intervals(v)).relaxed
for k, v in accesses.items() if k
}
dspace = DataSpace(c.dspace.intervals, parts)
processed.append(
c.rebuild(exprs=cexprs, ispace=ispace, dspace=dspace))
assert len(exprs) == 0
return processed
def derive_dspace(expr, ispace):
accesses = detect_accesses(expr)
parts = {
k: IntervalGroup(build_intervals(v)).relaxed
for k, v in accesses.items() if k
}
dspace = DataSpace(ispace.intervals, parts)
return dspace
def rebuild(cluster, others, aliases, subs):
# Rebuild the non-aliasing expressions
exprs = [uxreplace(e, subs) for e in others]
# Add any new ShiftedDimension to the IterationSpace
ispace = cluster.ispace.augment(aliases.index_mapper)
# Rebuild the DataSpace to include the new symbols
accesses = detect_accesses(exprs)
parts = {k: IntervalGroup(build_intervals(v)).relaxed
for k, v in accesses.items() if k}
dspace = DataSpace(cluster.dspace.intervals, parts)
return cluster.rebuild(exprs=exprs, ispace=ispace, dspace=dspace)
def rebuild(cluster, others, schedule, subs):
"""
Plug the optimized aliases into the input Cluster. This leads to creating
a new Cluster with suitable IterationSpace and DataSpace.
"""
exprs = [uxreplace(e, subs) for e in others]
ispace = cluster.ispace.augment(schedule.dmapper)
ispace = ispace.augment(schedule.rmapper)
accesses = detect_accesses(exprs)
parts = {k: IntervalGroup(build_intervals(v)).relaxed
for k, v in accesses.items() if k}
dspace = DataSpace(cluster.dspace.intervals, parts)
return cluster.rebuild(exprs=exprs, ispace=ispace, dspace=dspace)
def optimize_clusters_msds(clusters):
"""
Relax the clusters by letting the expressions defined over MultiSubDomains to
rather be computed over the entire domain. This increases the likelihood of
code lifting by later passes.
"""
processed = []
for c in clusters:
msds = [
d for d in c.ispace.itdimensions
if isinstance(d, MultiSubDimension)
]
if msds:
mapper = {d: d.root for d in msds}
exprs = [uxreplace(e, mapper) for e in c.exprs]
ispace = c.ispace.relaxed(msds)
accesses = detect_accesses(exprs)
parts = {
k: IntervalGroup(build_intervals(v)).relaxed
for k, v in accesses.items() if k
}
intervals = [i for i in c.dspace if i.dim not in msds]
dspace = DataSpace(intervals, parts)
guards = {mapper.get(d, d): v for d, v in c.guards.items()}
properties = {mapper.get(d, d): v for d, v in c.properties.items()}
syncs = {mapper.get(d, d): v for d, v in c.syncs.items()}
processed.append(
c.rebuild(exprs=exprs,
ispace=ispace,
dspace=dspace,
guards=guards,
properties=properties,
syncs=syncs))
else:
processed.append(c)
return processed
def _eliminate_inter_stencil_redundancies(self, cluster, template,
**kwargs):
"""
Search aliasing expressions and capture them into vector temporaries.
Examples
--------
1) temp = (a[x,y,z]+b[x,y,z])*c[t,x,y,z]
>>>
ti[x,y,z] = a[x,y,z] + b[x,y,z]
temp = ti[x,y,z]*c[t,x,y,z]
2) temp1 = 2.0*a[x,y,z]*b[x,y,z]
temp2 = 3.0*a[x,y,z+1]*b[x,y,z+1]
>>>
ti[x,y,z] = a[x,y,z]*b[x,y,z]
temp1 = 2.0*ti[x,y,z]
temp2 = 3.0*ti[x,y,z+1]
"""
# For more information about "aliases", refer to collect.__doc__
aliases = collect(cluster.exprs)
# Redundancies will be stored in space-varying temporaries
graph = FlowGraph(cluster.exprs)
time_invariants = {
v.rhs: graph.time_invariant(v)
for v in graph.values()
}
# Find the candidate expressions
processed = []
candidates = OrderedDict()
for k, v in graph.items():
# Cost check (to keep the memory footprint under control)
naliases = len(aliases.get(v.rhs))
cost = estimate_cost(v, True) * naliases
test0 = lambda: cost >= self.MIN_COST_ALIAS and naliases > 1
test1 = lambda: cost >= self.MIN_COST_ALIAS_INV and time_invariants[
v.rhs]
if test0() or test1():
candidates[v.rhs] = k
else:
processed.append(v)
# Create alias Clusters and all necessary substitution rules
# for the new temporaries
alias_clusters = []
subs = {}
for origin, alias in aliases.items():
if all(i not in candidates for i in alias.aliased):
continue
# The write-to Intervals
writeto = [
Interval(i.dim, *alias.relaxed_diameter.get(i.dim, (0, 0)))
for i in cluster.ispace.intervals if not i.dim.is_Time
]
writeto = IntervalGroup(writeto)
# Optimization: no need to retain a SpaceDimension if it does not
# induce a flow/anti dependence (below, `i.offsets` captures this, by
# telling how much halo will be needed to honour such dependences)
dep_inducing = [i for i in writeto if any(i.offsets)]
try:
index = writeto.index(dep_inducing[0])
writeto = IntervalGroup(writeto[index:])
except IndexError:
warning("Couldn't optimize some of the detected redundancies")
# Create a temporary to store `alias`
dimensions = [d.root for d in writeto.dimensions]
halo = [(abs(i.lower), abs(i.upper)) for i in writeto]
array = Array(name=template(),
dimensions=dimensions,
halo=halo,
dtype=cluster.dtype)
# Build up the expression evaluating `alias`
access = tuple(i.dim - i.lower for i in writeto)
expression = Eq(array[access], origin)
# Create the substitution rules so that we can use the newly created
# temporary in place of the aliasing expressions
for aliased, distance in alias.with_distance:
assert all(i.dim in distance.labels for i in writeto)
access = [i.dim - i.lower + distance[i.dim] for i in writeto]
if aliased in candidates:
# It would *not* be in `candidates` if part of a composite alias
subs[candidates[aliased]] = array[access]
subs[aliased] = array[access]
# Construct the `alias` IterationSpace
intervals, sub_iterators, directions = cluster.ispace.args
ispace = IterationSpace(intervals.add(writeto), sub_iterators,
directions)
# Construct the `alias` DataSpace
mapper = detect_accesses(expression)
parts = {
k: IntervalGroup(build_intervals(v)).add(ispace.intervals)
for k, v in mapper.items() if k
}
dspace = DataSpace(cluster.dspace.intervals, parts)
# Create a new Cluster for `alias`
alias_clusters.append(Cluster([expression], ispace, dspace))
# Switch temporaries in the expression trees
processed = [e.xreplace(subs) for e in processed]
return alias_clusters + [cluster.rebuild(processed)]
def process(cluster, chosen, aliases, sregistry, platform):
clusters = []
subs = {}
for alias, writeto, aliaseds, distances in aliases.iter(cluster.ispace):
if all(i not in chosen for i in aliaseds):
continue
# The Dimensions defining the shape of Array
# Note: with SubDimensions, we may have the following situation:
#
# for zi = z_m + zi_ltkn; zi <= z_M - zi_rtkn; ...
# r[zi] = ...
#
# Instead of `r[zi - z_m - zi_ltkn]` we have just `r[zi]`, so we'll need
# as much room as in `zi`'s parent to avoid going OOB
# Aside from ugly generated code, the reason we do not rather shift the
# indices is that it prevents future passes to transform the loop bounds
# (e.g., MPI's comp/comm overlap does that)
dimensions = [d.parent if d.is_Sub else d for d in writeto.dimensions]
# The halo of the Array
halo = [(abs(i.lower), abs(i.upper)) for i in writeto]
# The data sharing mode of the Array
sharing = 'local' if any(d.is_Incr for d in writeto.dimensions) else 'shared'
# Finally create the temporary Array that will store `alias`
array = Array(name=sregistry.make_name(), dimensions=dimensions, halo=halo,
dtype=cluster.dtype, sharing=sharing)
# The access Dimensions may differ from `writeto.dimensions`. This may
# happen e.g. if ShiftedDimensions are introduced (`a[x,y]` -> `a[xs,y]`)
adims = [aliases.index_mapper.get(d, d) for d in writeto.dimensions]
# The expression computing `alias`
adims = [aliases.index_mapper.get(d, d) for d in writeto.dimensions] # x -> xs
indices = [d - (0 if writeto[d].is_Null else writeto[d].lower) for d in adims]
expression = Eq(array[indices], uxreplace(alias, subs))
# Create the substitution rules so that we can use the newly created
# temporary in place of the aliasing expressions
for aliased, distance in zip(aliaseds, distances):
assert all(i.dim in distance.labels for i in writeto)
indices = [d - i.lower + distance[i.dim] for d, i in zip(adims, writeto)]
subs[aliased] = array[indices]
if aliased in chosen:
subs[chosen[aliased]] = array[indices]
else:
# Perhaps part of a composite alias ?
pass
# Construct the `alias` IterationSpace
ispace = cluster.ispace.add(writeto).augment(aliases.index_mapper)
# Optimization: if possible, the innermost IterationInterval is
# rounded up to a multiple of the vector length
try:
it = ispace.itintervals[-1]
if ROUNDABLE in cluster.properties[it.dim]:
vl = platform.simd_items_per_reg(cluster.dtype)
ispace = ispace.add(Interval(it.dim, 0, it.interval.size % vl))
except (TypeError, KeyError):
pass
# Construct the `alias` DataSpace
accesses = detect_accesses(expression)
parts = {k: IntervalGroup(build_intervals(v)).add(ispace.intervals).relaxed
for k, v in accesses.items() if k}
dspace = DataSpace(cluster.dspace.intervals, parts)
# Finally, build a new Cluster for `alias`
built = cluster.rebuild(exprs=expression, ispace=ispace, dspace=dspace)
clusters.insert(0, built)
return clusters, subs
def lower_schedule(cluster, schedule, sregistry, options):
"""
Turn a Schedule into a sequence of Clusters.
"""
ftemps = options['cire-ftemps']
if ftemps:
make = TempFunction
else:
# Typical case -- the user does *not* "see" the CIRE-created temporaries
make = Array
clusters = []
subs = {}
for alias, writeto, ispace, aliaseds, indicess in schedule:
# Basic info to create the temporary that will hold the alias
name = sregistry.make_name()
dtype = cluster.dtype
if writeto:
# The Dimensions defining the shape of Array
# Note: with SubDimensions, we may have the following situation:
#
# for zi = z_m + zi_ltkn; zi <= z_M - zi_rtkn; ...
# r[zi] = ...
#
# Instead of `r[zi - z_m - zi_ltkn]` we have just `r[zi]`, so we'll need
# as much room as in `zi`'s parent to avoid going OOB
# Aside from ugly generated code, the reason we do not rather shift the
# indices is that it prevents future passes to transform the loop bounds
# (e.g., MPI's comp/comm overlap does that)
dimensions = [
d.parent if d.is_Sub else d for d in writeto.itdimensions
]
# The halo must be set according to the size of writeto space
halo = [(abs(i.lower), abs(i.upper)) for i in writeto]
# The indices used to write into the Array
indices = []
for i in writeto:
try:
# E.g., `xs`
sub_iterators = writeto.sub_iterators[i.dim]
assert len(sub_iterators) == 1
indices.append(sub_iterators[0])
except KeyError:
# E.g., `z` -- a non-shifted Dimension
indices.append(i.dim - i.lower)
obj = make(name=name,
dimensions=dimensions,
halo=halo,
dtype=dtype)
expression = Eq(obj[indices], alias)
callback = lambda idx: obj[idx]
else:
# Degenerate case: scalar expression
assert writeto.size == 0
obj = Symbol(name=name, dtype=dtype)
expression = Eq(obj, alias)
callback = lambda idx: obj
# Create the substitution rules for the aliasing expressions
subs.update({
aliased: callback(indices)
for aliased, indices in zip(aliaseds, indicess)
})
# Construct the `alias` DataSpace
accesses = detect_accesses(expression)
parts = {
k: IntervalGroup(build_intervals(v)).add(ispace.intervals).relaxed
for k, v in accesses.items() if k
}
dspace = DataSpace(cluster.dspace.intervals, parts)
# Drop or weaken parallelism if necessary
properties = dict(cluster.properties)
for d, v in cluster.properties.items():
if any(i.is_Modulo for i in ispace.sub_iterators[d]):
properties[d] = normalize_properties(v, {SEQUENTIAL})
elif d not in writeto.dimensions:
properties[d] = normalize_properties(v, {PARALLEL_IF_PVT})
# Finally, build the `alias` Cluster
clusters.append(
cluster.rebuild(exprs=expression,
ispace=ispace,
dspace=dspace,
properties=properties))
return clusters, subs
def _eliminate_inter_stencil_redundancies(self, cluster, template,
**kwargs):
"""
Search for redundancies across the expressions and expose them
to the later stages of the optimisation pipeline by introducing
new temporaries of suitable rank.
Two type of redundancies are sought:
* Time-invariants, and
* Across different space points
Examples
========
Let ``t`` be the time dimension, ``x, y, z`` the space dimensions. Then:
1) temp = (a[x,y,z]+b[x,y,z])*c[t,x,y,z]
>>>
ti[x,y,z] = a[x,y,z] + b[x,y,z]
temp = ti[x,y,z]*c[t,x,y,z]
2) temp1 = 2.0*a[x,y,z]*b[x,y,z]
temp2 = 3.0*a[x,y,z+1]*b[x,y,z+1]
>>>
ti[x,y,z] = a[x,y,z]*b[x,y,z]
temp1 = 2.0*ti[x,y,z]
temp2 = 3.0*ti[x,y,z+1]
"""
if cluster.is_sparse:
return cluster
# For more information about "aliases", refer to collect.__doc__
mapper, aliases = collect(cluster.exprs)
# Redundancies will be stored in space-varying temporaries
g = cluster.trace
indices = g.space_indices
time_invariants = {v.rhs: g.time_invariant(v) for v in g.values()}
# Find the candidate expressions
processed = []
candidates = OrderedDict()
for k, v in g.items():
# Cost check (to keep the memory footprint under control)
naliases = len(mapper.get(v.rhs, []))
cost = estimate_cost(v, True) * naliases
if cost >= self.thresholds['min-cost-alias'] and\
(naliases > 1 or time_invariants[v.rhs]):
candidates[v.rhs] = k
else:
processed.append(v)
# Create alias Clusters and all necessary substitution rules
# for the new temporaries
alias_clusters = ClusterGroup()
rules = OrderedDict()
for origin, alias in aliases.items():
if all(i not in candidates for i in alias.aliased):
continue
# Construct an iteration space suitable for /alias/
intervals, sub_iterators, directions = cluster.ispace.args
intervals = [
Interval(i.dim, *alias.relaxed_diameter.get(i.dim, i.limits))
for i in cluster.ispace.intervals
]
ispace = IterationSpace(intervals, sub_iterators, directions)
# Optimization: perhaps we can lift the cluster outside the time dimension
if all(time_invariants[i] for i in alias.aliased):
ispace = ispace.project(lambda i: not i.is_Time)
# Build a symbolic function for /alias/
intervals = ispace.intervals
halo = [(abs(intervals[i].lower), abs(intervals[i].upper))
for i in indices]
function = Array(name=template(), dimensions=indices, halo=halo)
access = tuple(i - intervals[i].lower for i in indices)
expression = Eq(Indexed(function.indexed, *access), origin)
# Construct a data space suitable for /alias/
mapper = detect_accesses(expression)
parts = {
k: IntervalGroup(build_intervals(v)).add(intervals)
for k, v in mapper.items() if k
}
dspace = DataSpace([i.zero() for i in intervals], parts)
# Create a new Cluster for /alias/
alias_clusters.append(Cluster([expression], ispace, dspace))
# Add substitution rules
for aliased, distance in alias.with_distance:
access = [
i - intervals[i].lower + j for i, j in distance
if i in indices
]
temporary = Indexed(function.indexed, *tuple(access))
rules[candidates[aliased]] = temporary
rules[aliased] = temporary
# Group clusters together if possible
alias_clusters = groupby(alias_clusters).finalize()
alias_clusters.sort(key=lambda i: i.is_dense)
# Switch temporaries in the expression trees
processed = [e.xreplace(rules) for e in processed]
return alias_clusters + [cluster.rebuild(processed)]
def _eliminate_inter_stencil_redundancies(self, cluster, template, **kwargs):
"""
Search for redundancies across the expressions and expose them
to the later stages of the optimisation pipeline by introducing
new temporaries of suitable rank.
Two type of redundancies are sought:
* Time-invariants, and
* Across different space points
Examples
========
Let ``t`` be the time dimension, ``x, y, z`` the space dimensions. Then:
1) temp = (a[x,y,z]+b[x,y,z])*c[t,x,y,z]
>>>
ti[x,y,z] = a[x,y,z] + b[x,y,z]
temp = ti[x,y,z]*c[t,x,y,z]
2) temp1 = 2.0*a[x,y,z]*b[x,y,z]
temp2 = 3.0*a[x,y,z+1]*b[x,y,z+1]
>>>
ti[x,y,z] = a[x,y,z]*b[x,y,z]
temp1 = 2.0*ti[x,y,z]
temp2 = 3.0*ti[x,y,z+1]
"""
if cluster.is_sparse:
return cluster
# For more information about "aliases", refer to collect.__doc__
mapper, aliases = collect(cluster.exprs)
# Redundancies will be stored in space-varying temporaries
g = cluster.trace
indices = g.space_indices
time_invariants = {v.rhs: g.time_invariant(v) for v in g.values()}
# Find the candidate expressions
processed = []
candidates = OrderedDict()
for k, v in g.items():
# Cost check (to keep the memory footprint under control)
naliases = len(mapper.get(v.rhs, []))
cost = estimate_cost(v, True)*naliases
if cost >= self.MIN_COST_ALIAS and (naliases > 1 or time_invariants[v.rhs]):
candidates[v.rhs] = k
else:
processed.append(v)
# Create alias Clusters and all necessary substitution rules
# for the new temporaries
alias_clusters = ClusterGroup()
rules = OrderedDict()
for origin, alias in aliases.items():
if all(i not in candidates for i in alias.aliased):
continue
# Construct an iteration space suitable for /alias/
intervals, sub_iterators, directions = cluster.ispace.args
intervals = [Interval(i.dim, *alias.relaxed_diameter.get(i.dim, i.limits))
for i in cluster.ispace.intervals]
ispace = IterationSpace(intervals, sub_iterators, directions)
# Optimization: perhaps we can lift the cluster outside the time dimension
if all(time_invariants[i] for i in alias.aliased):
ispace = ispace.project(lambda i: not i.is_Time)
# Build a symbolic function for /alias/
intervals = ispace.intervals
halo = [(abs(intervals[i].lower), abs(intervals[i].upper)) for i in indices]
function = Array(name=template(), dimensions=indices, halo=halo)
access = tuple(i - intervals[i].lower for i in indices)
expression = Eq(function[access], origin)
# Construct a data space suitable for /alias/
mapper = detect_accesses(expression)
parts = {k: IntervalGroup(build_intervals(v)).add(intervals)
for k, v in mapper.items() if k}
dspace = DataSpace([i.zero() for i in intervals], parts)
# Create a new Cluster for /alias/
alias_clusters.append(Cluster([expression], ispace, dspace))
# Add substitution rules
for aliased, distance in alias.with_distance:
access = [i - intervals[i].lower + j for i, j in distance if i in indices]
rules[candidates[aliased]] = function[access]
rules[aliased] = function[access]
# Group clusters together if possible
alias_clusters = groupby(alias_clusters).finalize()
alias_clusters.sort(key=lambda i: i.is_dense)
# Switch temporaries in the expression trees
processed = [e.xreplace(rules) for e in processed]
return alias_clusters + [cluster.rebuild(processed)]
def lower_schedule(cluster, schedule, chosen, sregistry, options):
"""
Turn a Schedule into a sequence of Clusters.
"""
onstack = options['cire-onstack']
clusters = []
subs = {}
for alias, writeto, ispace, aliaseds, indicess in schedule:
if all(i not in chosen for i in aliaseds):
continue
# The Dimensions defining the shape of Array
# Note: with SubDimensions, we may have the following situation:
#
# for zi = z_m + zi_ltkn; zi <= z_M - zi_rtkn; ...
# r[zi] = ...
#
# Instead of `r[zi - z_m - zi_ltkn]` we have just `r[zi]`, so we'll need
# as much room as in `zi`'s parent to avoid going OOB
# Aside from ugly generated code, the reason we do not rather shift the
# indices is that it prevents future passes to transform the loop bounds
# (e.g., MPI's comp/comm overlap does that)
dimensions = [d.parent if d.is_Sub else d for d in writeto.itdimensions]
halo = [(abs(i.lower), abs(i.upper)) for i in writeto]
# The data sharing mode of the Array. It can safely be `shared` only if
# all of the PARALLEL `cluster` Dimensions appear in `writeto`
parallel = [d for d, v in cluster.properties.items() if PARALLEL in v]
sharing = 'shared' if set(parallel) == set(writeto.itdimensions) else 'local'
# The memory region of the Array. On the heap, unless the user has
# explicitly requested allocation on the stack
scope = 'stack' if onstack else 'heap'
array = Array(name=sregistry.make_name(), dimensions=dimensions, halo=halo,
dtype=cluster.dtype, scope=scope, sharing=sharing)
indices = []
for i in writeto:
try:
# E.g., `xs`
sub_iterators = writeto.sub_iterators[i.dim]
assert len(sub_iterators) == 1
indices.append(sub_iterators[0])
except KeyError:
# E.g., `z` -- a non-shifted Dimension
indices.append(i.dim - i.lower)
expression = Eq(array[indices], alias)
# Create the substitution rules so that we can use the newly created
# temporary in place of the aliasing expressions
for aliased, indices in zip(aliaseds, indicess):
subs[aliased] = array[indices]
if aliased in chosen:
subs[chosen[aliased]] = array[indices]
else:
# Perhaps part of a composite alias ?
pass
# Construct the `alias` DataSpace
accesses = detect_accesses(expression)
parts = {k: IntervalGroup(build_intervals(v)).add(ispace.intervals).relaxed
for k, v in accesses.items() if k}
dspace = DataSpace(cluster.dspace.intervals, parts)
# Drop parallelism if using ModuloDimensions (due to rotations)
properties = dict(cluster.properties)
for d, v in cluster.properties.items():
if any(i.is_Modulo for i in ispace.sub_iterators[d]):
properties[d] = normalize_properties(v, {SEQUENTIAL})
# Finally, build the `alias` Cluster
clusters.append(cluster.rebuild(exprs=expression, ispace=ispace,
dspace=dspace, properties=properties))
return clusters, subs
def process(candidates, aliases, cluster, template):
"""
Create Clusters from aliasing expressions.
"""
clusters = []
subs = {}
for origin, alias in aliases.items():
if all(i not in candidates for i in alias.aliased):
continue
# The write-to Intervals
writeto = [
Interval(i.dim, *alias.relaxed_diameter.get(i.dim, (0, 0)))
for i in cluster.ispace.intervals if not i.dim.is_Time
]
writeto = IntervalGroup(writeto)
# Optimization: no need to retain a SpaceDimension if it does not
# induce a flow/anti dependence (below, `i.offsets` captures this, by
# telling how much halo will be required to honour such dependences)
dep_inducing = [i for i in writeto if any(i.offsets)]
try:
index = writeto.index(dep_inducing[0])
writeto = IntervalGroup(writeto[index:])
except IndexError:
perf_adv("Could not optimize some of the detected redundancies")
# Create a temporary to store `alias`
dimensions = [d.root for d in writeto.dimensions]
halo = [(abs(i.lower), abs(i.upper)) for i in writeto]
array = Array(name=template(),
dimensions=dimensions,
halo=halo,
dtype=cluster.dtype)
# Build up the expression evaluating `alias`
access = tuple(i.dim - i.lower for i in writeto)
expression = Eq(array[access], origin.xreplace(subs))
# Create the substitution rules so that we can use the newly created
# temporary in place of the aliasing expressions
for aliased, distance in alias.with_distance:
assert all(i.dim in distance.labels for i in writeto)
access = [i.dim - i.lower + distance[i.dim] for i in writeto]
if aliased in candidates:
# It would *not* be in `candidates` if part of a composite alias
subs[candidates[aliased]] = array[access]
subs[aliased] = array[access]
# Construct the `alias` IterationSpace
intervals, sub_iterators, directions = cluster.ispace.args
ispace = IterationSpace(intervals.add(writeto), sub_iterators,
directions)
# Optimize the `alias` IterationSpace: if possible, the innermost
# IterationInterval is rounded up to a multiple of the vector length
try:
it = ispace.itintervals[-1]
if ROUNDABLE in cluster.properties[it.dim]:
from devito.parameters import configuration
vl = configuration['platform'].simd_items_per_reg(
cluster.dtype)
ispace = ispace.add(Interval(it.dim, 0, it.interval.size % vl))
except (TypeError, KeyError):
pass
# Construct the `alias` DataSpace
mapper = detect_accesses(expression)
parts = {
k: IntervalGroup(build_intervals(v)).add(ispace.intervals)
for k, v in mapper.items() if k
}
dspace = DataSpace(cluster.dspace.intervals, parts)
# Create a new Cluster for `alias`
clusters.append(
cluster.rebuild(exprs=[expression], ispace=ispace, dspace=dspace))
return clusters, subs
