Connections involving distributed variables

Connections involving distributed variables#

The purpose of this section is to describe the behavior you should expect when connecting two variables together when at least one of them is a distributed variable.

Distributed output to non-distributed input#

Because distributed variables may have different sizes and/or values on different ranks, and non-distributed variables have the same value and size on all ranks, the only way that a distributed to non-distributed connection is allowed is if src_indices are specified for the non-distributed input and that the specified src_indices are identical on all ranks where the input variable exists. Otherwise the non-distributed inputs could have different values on different ranks, which is illegal. Note that in previous versions of OpenMDAO, the behavior when connecting a distributed output to a non-distributed input when src_indices were not specified was that the non-distributed input on each rank would be the same size as the full distributed output. This is no longer the case. In fact, this case is no longer allowed. However, you can still achieve the same behavior by specifying src_indices of om.slicer[:] on the input, which explicitly specifies that the input on each rank is the same size as the full distributed output. For example:

group.connect('mydistcomp.out', 'mynondistributed.in', src_indices=om.slicer[:])

Distributed output to distributed input#

When connecting two distributed variables, you can specify src_indices on the input however you choose, or OpenMDAO will automatically assign src_indices to the input based on the size of the input in each rank. For example, if the component that owns the input is running on 3 ranks with input sizes of [2, 3, 4], then src_indices of [0, 1], [2, 3, 4], and [5, 6, 7, 8] will be specified on the 3 ranks. If, however, the global size of the output does not equal the global size of the input, an exception will be raised saying that OpenMDAO is not able to determine the src_indices.

Non-distributed output to distributed input#

These types of connections are deprecated and will become errors in a future release, but for now, you are allowed to connect a non-distributed output to a distributed input, provided that the global sizes of input and output are equal and the inputs are the same size on every rank.

Example#

The following example shows the MixedDistrib2 component, which contains a non-distributed input, a distributed input, and a distributed output. We show how to connect the non-distributed input and the distributed input to a distributed output.

Note

This feature requires MPI, and may not be able to be run on Colab or Binder.

%%px

import numpy as np

import openmdao.api as om
from openmdao.utils.array_utils import evenly_distrib_idxs
from openmdao.utils.mpi import MPI


class MixedDistrib2(om.ExplicitComponent):

    def initialize(self):
        self.options.declare('vec_size', types=int, default=1,
                             desc="Total size of vector.")

    def setup(self):
        comm = self.comm
        rank = comm.rank

        size = self.options['vec_size']
        sizes, _ = evenly_distrib_idxs(comm.size, size)
        mysize = sizes[rank]

        # Distributed Input
        self.add_input('in_dist', np.ones(mysize, float), distributed=True)

        # Non-Distributed Input
        self.add_input('in_nd', np.ones(size, float))

        # Distributed Output
        self.add_output('out_dist', copy_shape='in_dist', distributed=True)

    def compute(self, inputs, outputs):
        x = inputs['in_dist']
        y = inputs['in_nd']

        # "Computationally Intensive" operation that we wish to parallelize.
        f_x = x**2 - 2.0*x + 4.0

        # This operation is repeated on all procs.
        f_y = y ** 0.5

        outputs['out_dist'] = f_x + np.sum(f_y)

size = 7

if MPI:
    comm = MPI.COMM_WORLD
    rank = comm.rank
    sizes, offsets = evenly_distrib_idxs(comm.size, size)
else:
    # When running without MPI, the entire variable is on one proc.
    rank = 0
    sizes = {rank : size}
    offsets = {rank : 0}

prob = om.Problem()
model = prob.model

# Create a distributed source for the distributed input.
ivc = om.IndepVarComp()
ivc.add_output('x_dist', np.zeros(sizes[rank]), distributed=True)
ivc.add_output('x_nd', np.zeros(size))

model.add_subsystem("indep", ivc)
model.add_subsystem("D1", MixedDistrib2(vec_size=size))
model.add_subsystem("D2", MixedDistrib2(vec_size=size))

model.connect('indep.x_dist', 'D1.in_dist')
model.connect('indep.x_nd', 'D1.in_nd')

# Distributed output to distributed input.
model.connect('D1.out_dist', 'D2.in_dist')

# Distributed output to non-distributed input.
# The Slicer is used to select all elements in D1.out_dist.
model.connect('D1.out_dist', 'D2.in_nd', src_indices=om.slicer[:])


prob.setup(force_alloc_complex=True)

# Set initial values of distributed variable.
x_dist_init = 3.0 + np.arange(size)[offsets[rank]:offsets[rank] + sizes[rank]]
prob.set_val('indep.x_dist', x_dist_init)

# Set initial values of non-distributed variable.
x_nd_init = 1.0 + 2.0*np.arange(size)
prob.set_val('indep.x_nd', x_nd_init)

prob.run_model()

# Values on each rank.
for var in ['D1.out_dist', 'D2.in_dist']:
    print(var, prob.get_val(var))
    
# Note: to get the value of a non-distributed input that is connected to a distributed output, you 
# need to set get_remote to True.
print('D2.in_nd', prob.get_val('D2.in_nd', get_remote=True))

[stdout:2] D1.out_dist [56.53604616 69.53604616]
D2.in_dist [56.53604616 69.53604616]
D2.in_nd [24.53604616 29.53604616 36.53604616 45.53604616 56.53604616 69.53604616
 84.53604616]

[stdout:3] D1.out_dist [84.53604616]
D2.in_dist [84.53604616]
D2.in_nd [24.53604616 29.53604616 36.53604616 45.53604616 56.53604616 69.53604616
 84.53604616]

[stdout:1] D1.out_dist [36.53604616 45.53604616]
D2.in_dist [36.53604616 45.53604616]
D2.in_nd [24.53604616 29.53604616 36.53604616 45.53604616 56.53604616 69.53604616
 84.53604616]

[stdout:0] D1.out_dist [24.53604616 29.53604616]
D2.in_dist [24.53604616 29.53604616]
D2.in_nd [24.53604616 29.53604616 36.53604616 45.53604616 56.53604616 69.53604616
 84.53604616]