Parallel job submission with SLURM

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Shared memory job (OpenMP)

In shared-memory parallel computing, multiple processors (or "threads") perform tasks independently but share a common global memory. If a processor modified an array in the memory, all other processors can see this update as well A serial code can be parallelized by marking code sections, which should be run in parallel, with openMP directives. An openMP code can run on a single compute node and use up to maximum number of processors in that compute node, e.g., 24, 36, or 128 processors. To compile an openMP code: $ module load gcc/6.3.0 $ gcc -o hello_omp -fopenmp hello_omp.c To run an openMP code, define number of processors(threads) in $OMP_NUM_THREADS and force the program to run on a single node, where all of the cores share their memory, with a --nodes=1 option : $ export OMP_NUM_THREADS=8 $ sbatch -n 8 --nodes=1 --wrap="./hello_omp"

Distributed memory job (MPI)

In distributed-memory parallel computing, multiple processors (or "cores") perform tasks independently while each processor possesses its own private memory resource. If a processor modify an array in its memory, this processor has to communicate to other processors so that the others see this update. Massage Passing Interface (MPI) library implements distributed memory parallel computing which can be programmed in C, C++ and Fortran An MPI program can run on a single compute node as well as on multiple compute nodes To compile an MPI code, use MPI wrappers, e.g., mpicc for C code, mpif90 for Fortran code $ module load gcc/6.3.0 openmpi/4.0.2 $ mpicc -o mpi_hello_world mpi_hello_world.c To run an MPI program, launch the program with "mpirun" $ module load gcc/6.3.0 openmpi/4.0.2 $ sbatch -n 240 --wrap="mpirun ./mpi_hello_world"

Examples

• openMP hello world in C
• MPI hello world in C

Further readings

• Parallel computing
• Four-day course in parallel programming with MPI/OpenMP at ETH

Helper

• SLURM Submission Line Advisor

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