OpenMP for shared memory programming#


OpenMP (Open Multi-Processing) is an API that supports multi-platform shared memory multiprocessing programming in C, C++, and Fortran, on most processor architectures and operating systems. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.

OpenMP uses a portable, scalable model that gives programmers a simple and flexible interface for developing parallel applications for platforms ranging from the standard desktop computer to the supercomputer. The current version of the OpenMP specification is 5.1, released in November 2020.

However, not all compilers already fully support this standard. The previous specification were OpenMP 5.0 (November 2018), OpenMP 4.5 (November 2015) and OpenMP 4.0 (July 2013).


You should have a program that uses the OpenMP API.


On the VSC clusters, the following compilers support OpenMP:

Intel compilers in the Intel toolchain

The Intel compiler version 18.0 (intel/2018a and intel/2018b toolchains) offers almost complete OpenMP 4.5 support.

GCC compilers in the FOSS toolchain

GCC 6.x (foss/2018a) offers full OpenMP 4.5 support in C and C++, including offloading to some variants of the Xeon Phi and to AMD HSAIL and some support for OpenACC on NVIDIA. For Fortran, OpenMP 4.0 is supported.

For an overview of compiler (version) support for the various OpenMP specifications, see the OpenMP compilers and tools page.


The GCC OpenMP runtime is for most applications inferior to the Intel implementation.

Compiling OpenMP code#

See the instructions on the page about toolchains for compiling OpenMP code with the Intel and GCC compilers.


It is in fact possible to link OpenMP object code compiled with GCC and the Intel compiler on the condition that the Intel OpenMP libraries and run-time is used (e.g., by linking using icc with the -qopenmp option), but the Intel manual is not clear which versions of gcc and icc work together well. This is only for specialists but may be useful if you only have access to object files and not to the full source code.

Running OpenMP programs#

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

Since OpenMP is intended for use in a shared memory context, when submitting a job to the queue system, remember to request a single node and as many processors as you need parallel threads (e.g., -l nodes=1:ppn=4). The latter should not exceed the number of cores on the machine the job runs on. For relevant hardware information, please consult the list of available hardware.

You may have to set the number of cores that the program should use by hand, e.g., when you don’t use all cores on a node, because the OpenMP runtime recognizes the number of cores available on the node, and not respect the number of cores assigned to the job.

Depending on the program, this may be through a command line option to the executable, a value in the input file or the environment variable OMP_NUM_THREADS.


Failing to set this value may result in threads competing with each other for resources such as cache and access to the CPU and thus (much) lower performance.

Further information#

  • OpenMP contains the specifications and some documentation. It is the web site of the OpenMP Architecture Review Board where the standard is discussed.

  • See also the pages in the tutorials section and online tutorials.

The tutorial at the site of Lawrence Livermore National Laboratory LLNL OpenMP tutorial (LLNL) is highly recommended.