Attention

WARNING: From 8am on 30th June until 5pm on 4th July 2025, there will be no access to the Stanage HPC cluster.

We will try to minimise this downtime and it is possible that Stanage will return to service before 4th July. We will notify you by email when Stanage is back online and available for job submission.

Message Passing Interface (MPI)

The Message Passing Interface is a standard for passing data and other messages between running processes which may or may not be on a single computer. It is commonly used on computer clusters as a means by which a set of related processes can work together in parallel on one or more tasks. These strands (processes) must therefore communicate data and other information by passing messages between each other.

MPI is used on systems ranging from a few interconnected Raspberry Pi’s through to the UK’s national supercomputer, Archer.

MPI Implementations

The Message Passing Interface (MPI) itself is just a specification for a message passing library.

There are multiple implementations of this specification, each produced by a different organisation, including OpenMPI and Intel MPI. This documentation includes information on the MPI implementations available on Stanage and Bessemer. On the Stanage cluster these implementations have been compiled in a way that allows them to make optimal use of the high-speed network infrastructure (OmniPath). If you are not sure which implementation to use then try the latest available version of OpenMPI.

Batch MPI

To use MPI you need use module load to activate a particular MPI implementation (or module load an application that itself loads an MPI implementation behind the scenes).

Here is an example that requests 4 slots (CPU cores) with 8GB of RAM per slot then runs a program called executable in the current directory using the OpenMPI library (version 4.1.4, built using version 12.2.0 of the gcc compiler). It is assumed that executable was previously compiled using that exact same MPI library.

#!/bin/bash
# Request one node
#SBATCH --nodes=1
# Request 4 cores per node
#SBATCH ntasks=4
# Request 8GB of RAM per node
#SBATCH mem=8G

# Load a MPI library
module load OpenMPI/4.1.4-GCC-12.2.0

# Run a program previously compiled using that specific MPI library
srun --export=ALL ./executable

MPI Training

Training courses from the national supercomputing centre are available here