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June 23, 2020 13:59
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import openmdao.api as om | |
import numpy as np | |
import mpi4py.MPI as MPI | |
# RUN THIS WITH 2 or more PROCS UNDER MPI | |
SRC_INDICES = [1,2] | |
FORCE_PETSCTRANSFER = True | |
# this should raise an error when COMP_DISTRIBUTED is false | |
# this should work when COMP_DISTRIBUTED is true | |
COMP_DISTRIBUTED = True | |
class TestCompDist(om.ExplicitComponent): | |
# this comp is distributed and forces PETScTransfer | |
def initialize(self): | |
self.options['distributed'] = True | |
def setup(self): | |
self.add_input('x', shape=2) | |
self.add_output('y', shape=1) | |
self.declare_partials('y', 'x', val=1.0) | |
def compute(self, inputs, outputs): | |
outputs['y'] = np.sum(inputs['x']) | |
class TestComp(om.ExplicitComponent): | |
def initialize(self): | |
self.options['distributed'] = COMP_DISTRIBUTED | |
def setup(self): | |
# read SRC_INDICES on each proc | |
self.add_input('x', shape=2, src_indices=SRC_INDICES, val=-2038.0) | |
self.add_output('y', shape=1) | |
self.declare_partials('y', 'x') | |
def compute(self, inputs, outputs): | |
outputs['y'] = np.sum(inputs['x']) | |
def compute_partials(self, inputs, J): | |
J['y', 'x'] = np.ones((2,)) | |
prob = om.Problem() | |
model = prob.model | |
# nobody should ever do this but... | |
if MPI.COMM_WORLD.rank == 0: | |
setval = np.array([2.0,3.0]) | |
else: | |
setval = np.array([10.0,20.0]) | |
# no parallel or distributed comps, so default_vector is used (local xfer only) | |
model.add_subsystem('p1', om.IndepVarComp('x', setval)) | |
model.add_subsystem('c3', TestComp()) | |
if FORCE_PETSCTRANSFER: | |
model.add_subsystem('c4', TestCompDist()) | |
model.connect("p1.x", "c3.x") | |
prob.setup(check=False, mode='fwd') | |
prob.run_model() | |
print('rank: ' + str(MPI.COMM_WORLD.rank) + ' val: ' + str(prob['c3.y']) + ' should be 13') | |
# list_outputs only shows the value on the first proc (0th) | |
prob.model.list_outputs() |
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