Openmdao - compute finite difference in parallel when optimizing - openmdao

Question

I have made a simple example (see below) which does the fd in serial. What is the best way to do this in parallel? I am using Python 3.6 and OpenMDAO 2.4.0.
import numpy as np
from openmdao.api import Problem, ScipyOptimizeDriver, ExecComp, IndepVarComp, ExplicitComponent, Group
class WorkFlow(ExplicitComponent):
def setup(self):
self.add_input('x', np.ones(5))
self.add_output('y', 2.0)
self.declare_partials('y', 'x', method='fd')
def compute(self, inputs, outputs):
print('comm:', self.comm.rank, inputs['x'])
print()
outputs['y'] = abs(np.sum(inputs['x']**2) - 9)
prob = Problem()
indeps = prob.model.add_subsystem('indeps', IndepVarComp(), promotes=['*'])
indeps.add_output('x', np.ones(5))
prob.model.add_subsystem('wf', WorkFlow(), promotes_inputs=['x'])
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.model.add_design_var('x', lower=-10.0, upper=10.0)
prob.model.add_objective('wf.y')
prob.setup()
prob.run_driver()
print(prob['x'])
print(prob['wf.y'])

Answer 1

As of OpenMDAO v2.4 you could not do parallel finite-difference. However, the feature has been recently added to the master branch of OpenMDAO and will be officially released in OpenMDAO V2.5 soon.
To use the feature right now, install the repository version of openMDAO (You can not do pip install openmdao. instead clone the repository from github and then pip install -e <location of the cloned repo>).
Then you can follow the instructions on the documentation page for parallel FD.
Here is your actual code though. The only change to the component is that when you instantiate it you provide the num_par_fd=5 argument to the component. Then when you call the file, you should run it under mpi like this:
mpiexec -n 5 python test.py
Here is what test.py should look like:
import numpy as np
from openmdao.api import Problem, ScipyOptimizeDriver, ExecComp, IndepVarComp, ExplicitComponent, Group
class WorkFlow(ExplicitComponent):
def setup(self):
self.add_input('x', np.ones(5))
self.add_output('y', 2.0)
self.declare_partials('y', 'x', method='fd')
def compute(self, inputs, outputs):
print('comm:', self.comm.rank, inputs['x'])
print()
outputs['y'] = abs(np.sum(inputs['x']**2) - 9)
prob = Problem()
indeps = prob.model.add_subsystem('indeps', IndepVarComp(), promotes=['*'])
indeps.add_output('x', np.ones(5))
prob.model.add_subsystem('wf', WorkFlow(num_par_fd=5), promotes_inputs=['x'])
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.model.add_design_var('x', lower=-10.0, upper=10.0)
prob.model.add_objective('wf.y')
prob.setup()
prob.run_driver()
print(prob['x'])
print(prob['wf.y'])