Parallel job submission

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 run an openMP code, define number of processors(threads) in $OMP_NUM_THREADS

$ export OMP_NUM_THREADS=8
$ bsub -n 8 ./program

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
An MPI program must be launched using "mpirun"

$ module load gcc/6.3.0 openmpi/4.0.2
$ bsub -n 240 mpirun ./program