## The problem

Designing and debugging a FEniCS code in a jupyter notebook is effortless. I love the interactive environment and the markdown support that allows writing equations inside the notebook; this helps debug the application for the correctness of the code. But, recently, I had to debug a portion of the code that was taking far too much time to run. Somehow, I had to parallelize that portion of the code itself in the notebook.

## The solution

• Install the package ipyparallel

• The tutorial presented in the documentation is quite straighforward. You need to only make a function out of the portion of the code that you need to parallelize and then call it using ipyparallel

import ipyparallel as ipp

def mpi_example():
from mpi4py import MPI
comm = MPI.COMM_WORLD
return f"Hello World from rank {comm.Get_rank()}. total ranks={comm.Get_size()}"

# request an MPI cluster with 4 engines
with ipp.Cluster(engines='mpi', n=4) as rc:
r = view.apply_sync(mpi_example)
print("\n".join(r))


## The execution

From the official documentation, it seems pretty straightforward. Parallelization is as simple as setting the number of processors to parallelize. But, from experience, I know that it is never this simple for custom applications. Anyhow, as a starting point, if I am not trying to get anything back from the parallel run, it should just work. Here is my first try with the Poisson equation.

import ipyparallel as ipp
import time

def poisson():

import dolfin as df
comm = df.MPI.comm_world

n = 600
mesh = df.RectangleMesh(comm, df.Point(0,0), df.Point(1,1), n, n)

V = df.FunctionSpace(mesh, "Lagrange", 1)
# Define Dirichlet boundary (x = 0 or x = 1)
boundary = df.CompiledSubDomain("near(x, 0) || near(x,1)")
# Define boundary condition
u0 = df.Constant(0.0)
bc = df.DirichletBC(V, u0, boundary)
# Define variational problem
u = df.TrialFunction(V)
v = df.TestFunction(V)
f = df.Expression("10*exp(-(pow(x - 0.5, 2) + pow(x - 0.5, 2)) / 0.02)",degree = 2)
g = df.Expression("sin(5*x)",degree=2)
L = f*v*df.dx + g*v*df.ds
# Compute solution
u = df.Function(V)
df.solve(a == L, u, bc)
with df.XDMFFile("output.xdmf") as xdmf:
xdmf.write(u)
return mesh.num_cells()

# request an MPI cluster with 4 engines
start = time.time()
with ipp.Cluster(engines='mpi', n=6) as rc:
r = view.apply_sync(poisson)
end = time.time()
print("Run time in seconds ----------------",int(end-start))

print("Elements with each processor: ",r)

Elements with each processor:  [119007, 119491, 122701, 117966, 120324, 120511]


As we can see from the output, each processor has a different portion of the mesh and thus a different number of elements. Right now, I am unable to see significant computational gains with parallelization. But, anyhow, this experiment is half successful.

## Reference

Using IPython for parallel computing