def get_distrib_idxs(self):
""" component declares the local sizes and sets initial values
for all distributed inputs and outputs. Returns a dict of
index arrays keyed to variable names.
"""
comm = self.mpi.comm
rank = comm.rank
self.sizes, self.offsets = evenly_distrib_idxs(comm.size,
self.arr_size)
start = self.offsets[rank]
end = start + self.sizes[rank]
#need to re-initialize the variable to have the correct local size
self.invec = np.ones(self.sizes[comm.rank], dtype=float)
return { 'invec': make_idx_array(start, end) }
def execute(self):
""" Run each parameter set. """
color = self._color[self.mpi.rank]
if color == MPI.UNDEFINED or self.mpi.comm == MPI.COMM_NULL:
return
# Prepare parameters and responses.
case_paths = {}
inputs = []
values = []
for path in self.get_parameters():
if isinstance(path, tuple):
for target in path:
inputs.append(target)
path = path[0]
else:
inputs.append(path)
val = self.case_inputs.get(make_legal_path(path))
values.append(val)
if not inputs:
return
length = len(values[0])
for path in self.get_responses():
case_paths[path] = make_legal_path(path)
sizes, offsets = evenly_distrib_idxs(self._num_parallel_subs, length)
start = offsets[color]
end = start + sizes[color]
self.init_responses(length)
# Run each parameter set.
for i in range(start, end):
# Set inputs.
for j, path in enumerate(inputs):
self.set_parameter_by_name(path, values[j][i])
# Run workflow.
with MPIContext():
self.run_iteration()
# Get outputs.
for path in self.get_responses():
cpath = case_paths[path]
self.case_outputs.get(cpath)[i] = self.parent.get(path)
if self._num_parallel_subs > 1:
# Now, collect the results back from all parallel processes
for path in self.get_responses():
path = case_paths[path]
vals = self.case_outputs.get(path)
if self._resp_comm != MPI.COMM_NULL:
allvals = self._resp_comm.gather(vals, root=0)
if self._resp_comm.rank == 0:
for i in range(self._num_parallel_subs):
vals[offsets[i]:offsets[i] +
sizes[i]] = allvals[i][offsets[i]:offsets[i] +
sizes[i]]
junk = self.mpi.comm.bcast(vals, root=0)
else:
vals = self.mpi.comm.bcast(None, root=0)
else:
vals = self.mpi.comm.bcast(vals, root=0)
self.case_outputs.set(path, vals)
def execute(self):
""" Run each parameter set. """
color = self._color[self.mpi.rank]
if color == MPI.UNDEFINED or self.mpi.comm == MPI.COMM_NULL:
return
# Prepare parameters and responses.
case_paths = {}
inputs = []
values = []
for path in self.get_parameters():
if isinstance(path, tuple):
for target in path:
inputs.append(target)
path = path[0]
else:
inputs.append(path)
val = self.case_inputs.get(make_legal_path(path))
values.append(val)
if not inputs:
return
length = len(values[0])
for path in self.get_responses():
case_paths[path] = make_legal_path(path)
sizes, offsets = evenly_distrib_idxs(self._num_parallel_subs,
length)
start = offsets[color]
end = start + sizes[color]
self.init_responses(length)
# Run each parameter set.
for i in range(start, end):
# Set inputs.
for j, path in enumerate(inputs):
self.set_parameter_by_name(path, values[j][i])
# Run workflow.
with MPIContext():
self.run_iteration()
# Get outputs.
for path in self.get_responses():
cpath = case_paths[path]
self.case_outputs.get(cpath)[i] = self.parent.get(path)
if self._num_parallel_subs > 1:
# Now, collect the results back from all parallel processes
for path in self.get_responses():
path = case_paths[path]
vals = self.case_outputs.get(path)
if self._resp_comm != MPI.COMM_NULL:
allvals = self._resp_comm.gather(vals, root=0)
if self._resp_comm.rank == 0:
for i in range(self._num_parallel_subs):
vals[offsets[i]:offsets[i]+sizes[i]] = allvals[i][offsets[i]:offsets[i]+sizes[i]]
junk = self.mpi.comm.bcast(vals, root=0)
else:
vals = self.mpi.comm.bcast(None, root=0)
else:
vals = self.mpi.comm.bcast(vals, root=0)
self.case_outputs.set(path, vals)