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Python Multiprocessing

Python's standard library provides a multiprocessing package that supports spawning of processes. This can be used to achieve some level of parallelism within a single compute node. It cannot be used to achieve parallelism across compute nodes. For that, users are referred to the discussion on mpi4py here.

If you are using the multiprocessing module, it is advised that you tell srun to use all the threads available on the node with the "-c" argument. For example, on Cori use:

srun -n 1 -c 64 python

Multiprocessing is a good solution, but not the best solution

Python multiprocessing achieves process-level parallelism through fork(). By default you can only expect multiprocessing to do a "pretty good" job of load-balancing tasks. For more fine-grained control of parallelism within a node, consider parallelism via Cython or writing C/C++/Fortran extensions that take advantage of OpenMP or threads.

Consider carefully whether multiprocessing is a good fit for your HPC application

Staff at various other centers go so far as to recommend strongly against using multiprocessing at all in an HPC context because of issues with affinity of forked processes; Python multiprocessing's shared memory model interacting poorly with many MPI implementations, threaded libraries, and libraries using shared memory; and debuggers and performance tools have trouble following forked processes. We suppose that it can have limited application in specific cases, provided users are informed of the issues.

Multiprocessing Interaction with OpenMP

If your multiprocessing code makes calls to a threaded library like numpy with threaded MKL support then you need to consider oversubscription of threads. While process affinity can be controlled to some degrees in certain contexts (e.g. Python distributions that implement os.sched_{get,set}affinity) it is generally easier to reduce the number of threads used by each process. Actually it is most advisable to set it to a single thread. In particular for OpenMP:


Furthermore, use of Python multiprocessing on KNL you are advised to specify:

export KMP_AFFINITY=disabled

Issues Combining Multiprocessing and MPI

Users have been able to combine Python multiprocessing and mpi4py to achieve hybrid parallelism on NERSC systems, but not without issues. If you decide to try to combine mpi4py and Python multiprocessing, be advised that on the NERSC Cray systems (Cray MPICH) one must set the following environment variable:


See the "mpi_intro" man-page for details. Again we advise that combining Python multiprocessing and mpi4py qualifies as a "hack" that may work for developers in the short term. Users are strongly encouraged to consider alternatives.