MPI

Setting Up MPI Environment

The Anselm cluster provides several implementations of the MPI library:

MPI Library Thread support
The highly optimized and stable bullxmpi 1.2.4.1 Partial thread support up to MPI_THREAD_SERIALIZED
The Intel MPI 4.1 Full thread support up to MPI_THREAD_MULTIPLE
The OpenMPI 1.6.5 Full thread support up to MPI_THREAD_MULTIPLE, BLCR c/r support
The OpenMPI 1.8.1 Full thread support up to MPI_THREAD_MULTIPLE, MPI-3.0 support
The mpich2 1.9 Full thread support up to MPI_THREAD_MULTIPLE, BLCR c/r support

MPI libraries are activated via the environment modules.

$ ml av mpi/
    ------------------------- /opt/modules/modulefiles/mpi -------------------------
    bullxmpi/bullxmpi-1.2.4.1 mvapich2/1.9-icc
    impi/4.0.3.008             openmpi/1.6.5-gcc(default)
    impi/4.1.0.024             openmpi/1.6.5-gcc46
    impi/4.1.0.030             openmpi/1.6.5-icc
    impi/4.1.1.036(default)    openmpi/1.8.1-gcc
    openmpi/1.8.1-gcc46
    mvapich2/1.9-gcc(default)  openmpi/1.8.1-gcc49
    mvapich2/1.9-gcc46         openmpi/1.8.1-icc

There are default compilers associated with any particular MPI implementation. The defaults may be changed, the MPI libraries may be used in conjunction with any compiler. The defaults are selected via the modules in following way

Module MPI Compiler suite
PrgEnv-gnu bullxmpi-1.2.4.1 bullx GNU 4.4.6
PrgEnv-intel Intel MPI 4.1.1 Intel 13.1.1
bullxmpi bullxmpi-1.2.4.1 none, select via module
impi Intel MPI 4.1.1 none, select via module
openmpi OpenMPI 1.6.5 GNU compilers 4.8.1, GNU compilers 4.4.6, Intel Compilers
openmpi OpenMPI 1.8.1 GNU compilers 4.8.1, GNU compilers 4.4.6, GNU compilers 4.9.0, Intel Compilers
mvapich2 MPICH2 1.9 GNU compilers 4.8.1, GNU compilers 4.4.6, Intel Compilers

Examples:

$ ml OpenMPI **or** ml openmpi **for older versions**

In this example, we activate the latest openmpi with latest GNU compilers

To use openmpi with the intel compiler suite, use

$ ml intel
$ ml openmpi/1.6.5-icc

In this example, the openmpi 1.6.5 using intel compilers is activated

Compiling MPI Programs

Note

After setting up your MPI environment, compile your program using one of the mpi wrappers

$ mpicc -v
$ mpif77 -v
$ mpif90 -v

Example program:

    // helloworld_mpi.c
    #include <stdio.h>

    #include<mpi.h>

    int main(int argc, char **argv) {

    int len;
    int rank, size;
    char node[MPI_MAX_PROCESSOR_NAME];

    // Initiate MPI
    MPI_Init(&argc, &argv);
    MPI_Comm_rank(MPI_COMM_WORLD,&rank);
    MPI_Comm_size(MPI_COMM_WORLD,&size);

    // Get hostame and print
    MPI_Get_processor_name(node,&len);
    printf("Hello world! from rank %d of %d on host %sn",rank,size,node);

    // Finalize and exit
    MPI_Finalize();

    return 0;
    }

Compile the above example with

$ mpicc helloworld_mpi.c -o helloworld_mpi.x

Running MPI Programs

Note

The MPI program executable must be compatible with the loaded MPI module. Always compile and execute using the very same MPI module.

It is strongly discouraged to mix mpi implementations. Linking an application with one MPI implementation and running mpirun/mpiexec form other implementation may result in unexpected errors.

The MPI program executable must be available within the same path on all nodes. This is automatically fulfilled on the /home and /scratch file system. You need to preload the executable, if running on the local scratch /lscratch file system.

Ways to Run MPI Programs

Optimal way to run an MPI program depends on its memory requirements, memory access pattern and communication pattern.

Note

Consider these ways to run an MPI program:

  1. One MPI process per node, 16 threads per process
  2. Two MPI processes per node, 8 threads per process
  3. 16 MPI processes per node, 1 thread per process.

One MPI process per node, using 16 threads, is most useful for memory demanding applications, that make good use of processor cache memory and are not memory bound. This is also a preferred way for communication intensive applications as one process per node enjoys full bandwidth access to the network interface.

Two MPI processes per node, using 8 threads each, bound to processor socket is most useful for memory bandwidth bound applications such as BLAS1 or FFT, with scalable memory demand. However, note that the two processes will share access to the network interface. The 8 threads and socket binding should ensure maximum memory access bandwidth and minimize communication, migration and NUMA effect overheads.

Note

Important! Bind every OpenMP thread to a core!

In the previous two cases with one or two MPI processes per node, the operating system might still migrate OpenMP threads between cores. You want to avoid this by setting the KMP_AFFINITY or GOMP_CPU_AFFINITY environment variables.

16 MPI processes per node, using 1 thread each bound to processor core is most suitable for highly scalable applications with low communication demand.

Running OpenMPI

The bullxmpi-1.2.4.1 and OpenMPI 1.6.5 are both based on OpenMPI. Read more on how to run OpenMPI based MPI.

Running MPICH2

The Intel MPI and mpich2 1.9 are MPICH2 based implementations. Read more on how to run MPICH2 based MPI.

The Intel MPI may run on the Intel Xeon Phi accelerators as well. Read more on how to run Intel MPI on accelerators.