Parallelism in Firedrake

Firedrake uses MPI for distributed memory parallelism. This is carried out transparently as long as your usage of Firedrake is only through the public API. To run your code in parallel you need you use the MPI job launcher available on your system. Often this program is called mpiexec. For example, to run a simulation in a file named on 16 processes we might use.

mpiexec -n 16 python

Expected performance improvements

Without detailed analysis, it is difficult to say precisely how much performance improvement should be expected from running in parallel. As a rule of thumb, it is worthwhile adding more processes as long as the number of degrees of freedom per process is more than around 50000. This is explored in some depth in the main Firedrake paper. Additionally, most of the finite element calculations performed by Firedrake are limited by the memory bandwidth of the machine. You can measure how the achieved memory bandwidth changes depending on the number of processes used on your machine using STREAMS.

Using MPI Communicators

By default, Firedrake parallelises across MPI_COMM_WORLD. If you want to perform a simulation in which different subsets of processes perform different computations (perhaps solving the same PDE for multiple different initial conditions), this can be achieved by using sub-communicators. The mechanism to do so is to provide a communicator when building the Mesh you will perform the simulation on, using the optional comm keyword argument. All subsequent operations using that mesh are then only collective over the supplied communicator, rather than MPI_COMM_WORLD. For example, to split the global communicator into two and perform two different simulations on the two halves we would write.

from firedrake import *

comm = COMM_WORLD.Split(COMM_WORLD.rank % 2)

if COMM_WORLD.rank % 2 == 0:
   # Even ranks create a quad mesh
   mesh = UnitSquareMesh(N, N, quadrilateral=True, comm=comm)
   # Odd ranks create a triangular mesh
   mesh = UnitSquareMesh(N, N, comm=comm)


To access the communicator a mesh was created on, we can use the mesh.comm property, or the function mesh.mpi_comm().