Parallelization

Parallelization is available using either distributed memory based on MPI or multithreading using OpenMP.

MPI

Todo

add some remarks on dune.fem.loadBalance and possibly some information on loadbalancing strategies available in dune-alugrid?

It is straightforward to use MPI for parallelization. It requires a parallel grid, in fact most of the DUNE grids work in parallel except albertaGrid or polyGrid. Most iterative solvers in DUNE work for parallel runs. Some of the preconditioning methods also work in parallel. Running a parallel job can be done by using mpirun

mpirun -np 4 python script.py

in order to use 4 MPI processes. Example scripts that run in parallel are, for example, the Re-entrant Corner Problem.

OpenMP

It is straightforward to enable some multithreading support. Note that this will speedup assembly and evaluation of the spatial operators but not in general the linear algebra backend so that the overall speedup of the code might not be as high as expected. Since we rely mostly on external packages for the linear algebra, speedup in this step will depend on the multithreading support available in the chosen linear algebra backend - see the discussion on how to switch between linear solver backends to, for example, use the thread parallel solvers from scipy.

By default only a single thread is used. To enable multithreading simply add

[1]:

from dune.fem import threading
print("Using",threading.use,"threads")
threading.use = 4 # use 4 threads
print("Using",threading.use,"threads")

Using 1 threads
Using 4 threads

At startup a maximum number of threads is selected based on the hardware concurrency. This number can be changed by setting the environment variable DUNE_NUM_THREADS which sets both the maximum and set the number of threads to use. To get this number or set the number of threads to use to the maximum use

[2]:

print("Maximum number of threads available:",threading.max)
threading.useMax()
print("Using",threading.use,"threads")

Maximum number of threads available: 32
Using 32 threads

This page was generated from the notebook parallelization_nb.ipynb and is part of the tutorial for the dune-fem python bindings DOI