Editing Portal:HPC - High Performance Computing (section)

=== MPI and message passing ===

'''MPI''', the '''Message Passing Interface''', is the dominant message-passing model in traditional distributed-memory HPC. The MPI Forum is the standardization body for MPI, and the MPI 5.0 standard describes point-to-point message passing, collective communication, group and communicator concepts, process topologies, environmental management, and process creation.<ref>MPI Forum, “MPI: A Message-Passing Interface Standard Version 5.0,” https://www.mpi-forum.org/docs/mpi-5.0/mpi50-report/mpi50-report.htm, accessed July 3, 2026.</ref>

MPI is the standard way that many large simulations communicate across nodes in a cluster. Unlike OpenMP, which usually targets shared memory within a node, MPI is designed for multiple processes that may run on different nodes and communicate by sending messages.

Perl has had MPI bindings. `Parallel::MPI` is described on MetaCPAN as a Perl interface to the MPI message-passing system, with authors Josh Wilmes and Chris Stevens.<ref>MetaCPAN, “Parallel::MPI,” https://metacpan.org/pod/Parallel%3A%3AMPI, accessed July 3, 2026.</ref>

There have also been references in the Perl science ecosystem to `PDL::Parallel::MPI`, listed by Perl 4 Science as an MPI-related PDL module.<ref>Perl 4 Science, “Modules and Projects,” https://perl4science.github.io/software/, accessed July 3, 2026.</ref>

Perl MPI support is historically important, but it should be described carefully. Perl is not a mainstream MPI application language today. Most production MPI codes are written in Fortran, C, or C++, sometimes with Python used for orchestration or analysis. Perl MPI bindings exist, but their maturity, maintainability, and compatibility with current MPI implementations must be checked for any new project.

Perl’s more common MPI role is orchestration. Perl can:

* generate MPI job scripts;
* launch MPI programs through `mpirun`, `mpiexec`, or Slurm `srun`;
* split input data into partitions;
* manage rank-specific files;
* parse MPI program output;
* detect failed ranks or failed nodes;
* perform post-processing after MPI jobs complete;
* coordinate ensembles of MPI simulations.

This is often the most practical way to use Perl in an MPI environment. Perl controls the workflow; the MPI executable performs the parallel numerical work.