# Running MPI Applications¶

• OpenMPI is provided by the foss - and iomkl toolchains; and may also be loaded directly. For available versions, type module avail OpenMPI/(note the slash). Normal way of loading is through the foss-toolchain module, e.g. module load foss/2018a

• Intel MPI environment is provided by the intel-toolchain and may also be loaded directly. For available versions, type module avail impi/. Normal way of loading is through the intel-toolchain module, e.g. module load intel/2018a

Also note that quite a few scientific packages is set up in such a way that all necessary software are loaded as a part of the software module in question. Do not load toolchains and/or mpi modules explicitely unless absolutely sure of the need for it!!!

Slurm is used as the queue system, and the native way to start MPI applications with Slurm is to use the srun command. On the other hand, both MPI implementations provide their own mechanisms to start application in the form of the mpirun command.

One of the most important factors when running large MPI jobs is mapping of the MPI ranks to compute nodes, and binding (or pinning) them to CPU cores. Neglecting to do that, or doing that in an suboptimal way can severely affect performance. In this regard there are some differences when it comes to running applications compiled against the two supported MPI environments.

Also note that the choice of MPI should be based on which MPI the code is compiled with support for. So if module list give you a OpenMPI/-reading, you should focus on the OpenMPI part beneath, if given a impi/-reading focus on the Intel MPI part

## OpenMPI¶

On systems with Mellanox InfiniBand, OpenMPI is the implementation recommended by Mellanox due to it’s support for the HPCX communication libraries.

### srun¶

With OpenMPI, srun is the preferred way to start MPI programs due to good integration with the Slurm scheduler environment:

srun /path/to/MySoftWare_exec


Executed as above, srun uses Slurm’s default binding and mapping algorithms (currently --cpu_bind=cores), which can be changed using either command-line parameters, or environment variables. Parameters specific to OpenMPI can be set using environment variables.

In the above scenario srun uses the PMI2 interface to launch the MPI ranks on the compute nodes, and to exchange the InfiniBand address information between the ranks. For large jobs the startup might be faster using OpenMPI’s PMIx method:

srun --mpi=pmix /path/to/MySoftWare_exec


The startup time might be improved further using the OpenMPI MCA pmix_base_async_modex argument (see below). With srun this needs to be set using an environment variable.

### mpirun¶

Warning

On Saga use srun, not mpirun

mpirun can get the number of tasks wrong and also lead to wrong task placement. We don’t fully understand why this happens. When using srun instead of mpirun or mpiexec, we observe correct task placement on Saga.

For those familiar with the OpenMPI tools, MPI applications can also be started using the mpirun command:

mpirun /path/to/MySoftWare_exec


By default, mpirun binds ranks to cores, and maps them by socket. Please refer to the documentation if you need to change those settings. Note that -report-bindings is a very useful option if you want to inspect the individual MPI ranks to see on which nodes, and on which CPU cores they run.

When launching large jobs with sparse communication patterns (neighbor to neighbor, local communication) the startup time will be improved by using the following command line argument:

mpirun -mca pmix_base_async_modex 1 ...


In the method above the address information will be exchanged between the ranks on a need-to-know basis, i.e., at first data exchange between two ranks, instead of an all to all communication step at program startup. Applications with dense communication patterns (peer to peer exchanges with all ranks) will likely experience a slowdown.

## Intel MPI¶

### mpirun¶

Warning

On Saga use srun, not mpirun

mpirun can get the number of tasks wrong and also lead to wrong task placement. We don’t fully understand why this happens. When using srun instead of mpirun or mpiexec, we observe correct task placement on Saga.

At this moment, for performance reasons mpirun is the preferred way to start applications that use Intel MPI:

mpirun /path/to/MySoftWare_exec


In the above, MySoftWare_exec is subject to mpirun’s internal mapping and binding algorithms. Intel’s mpirun uses it’s own default binding settings, which can be modified either by command line parameters, or by environment variables. Special care must be taken when running hybrid MPI-OpenMP cores. If this is your case, please refer to the documentation regarding Interoperability between MPI and OpenMP.

### srun¶

With srun, Intel MPI applications can be started as follows:

srun /path/to/MySoftWare_exec


We have observed that in the current setup some applications compiled against Intel MPI and executed with srun achieve inferior performance compared to the same code executed with mpirun. Until this is resolved, we suggest using mpirun to start applications.

## Final remarks¶

Note that when executing mpirun from within a Slurm allocation there is no need to provide neither the number of MPI ranks (-np), nor the host file (-hostfile): those are obtained automatically by mpirun. This is also be the case with srun.