MPI distributed programming#


MPI (Message Passing Interface) is a language-independent communications protocol used to program parallel computers. Both point-to-point and collective communication are supported. MPI “is a message-passing application programmer interface, together with protocol and semantic specifications for how its features must behave in any implementation.” MPI’s goals are high performance, scalability, and portability. MPI remains the dominant model used in high-performance computing today.

The current version of the MPI standard is 4.0, but hardly any implementations currently have support for the new features. All recent implementations support the full 3.1 standard though.

Some background information#

MPI-1.0 (1994) and its updates MPI-1.1 (1995), MPI-1.2 (1997) and MPI-1.3 (1998) concentrate on point-to-point communication (send/receive) and global operations in a static process topology. Major additions in MPI-2.0 (1997) and its updates MPI-2.1 (2008) and MPI-2.2 (2009) are one-sided communication (get/put), dynamic process management and a model for parallel I/O. MPI-3.0 (2012) adds non-blocking collectives, a major update of the one-sided communication model and neighborhood collectives on graph topologies. An update of the specification, MPI-3.1 was released in 2015. The MPI-4.0 was formally approved in June 2021.

The two dominant Open Source implementations are Open MPI and MPICH. The latter has been through a couple of name changes: It was originally conceived in the early ‘90’s as MPICH, then the complete rewrite was renamed to MPICH2, but as this name caused confusion as the MPI standard evolved into MPI 3.x, the name was changed again to MPICH, and the version number bumped to 3.0. MVAPICH developed at Ohio State University is the offspring of MPICH further optimized for InfiniBand and some other high-performance interconnect technologies. Most other MPI implementations are derived from one of these implementations.

At the VSC we offer both implementations: Open MPI is offered with the GNU compilers in the “FOSS toolchain” , while the Intel MPI used in the “Intel toolchain” is derived from the MPICH code base.


You have a program that uses an MPI library, either developed by you, or by others. In the latter case, the program’s documentation should mention the MPI library it was developed with.


On VSC clusters, several MPI implementations are installed. We provide two MPI implementations on all newer machines that implement the MPI-3.1 specification.

  1. Intel MPI in the Intel toolchain

  2. Open MPI in the FOSS toolchain

When developing your own software, this is the preferred order to select an implementation. The performance should be very similar, however, more development tools are available for Intel MPI (e.g., “Intel Trace Analyzer & Collector” for performance monitoring).

Several other implementations may be installed, e.g., MVAPICH, but we assume you know what you’re doing if you choose to use them.

We also assume you are already familiar with the job submission procedure. If not, check the “Running jobs in Slurm” section first.

Compiling and running#

See to the documentation about the Toolchains.


For debugging, we recommend the Arm DDT debugger (formerly Allinea DDT, module allinea-ddt). The debugger and the profiler Arm MAP (formerly Allinea MAP) are now bundled nito ArmForge, which is available as a module on KU Leuven systems. Video tutorials are available on the Arm website: ARM-DDT video. (KU Leuven-only).

When using the Intel toolchain, “Intel Trace Analyzer & Collector” (ITAC) may also prove useful.


To profile MPI applications, one may use Arm-MAP (formerly Allinea MAP) or Scalasca docs. (KU Leuven-only)

Further information#