A lot of scientific software, codes or libraries can be parallelized via MPI or OpenMP/Multiprocessing.

Avoid submitting inefficient Jobs!

If your code can be parallelized only paritially (serial parts remaining), familiarize with Amdahl's law and make sure your Job efficiency is still well above 50%.

Default Values

Slurm parameters like --ntasks and --cpus-per-task default to 1 if omitted.

Pure OpenMP Jobs (n CPUs)

Some software like the linear algebra routines in NumPy and MATLAB are able to use multiple CPU-cores via libraries that have been written using shared-memory parallel programming models like OpenMP,  pthreads or Intel Threading Building Blocks (TBB). OpenMP programs, for instance, run as multiple "threads" on a single node with each thread using one CPU-core.


Below is an appropriate Slurm script for a multithreaded job:

#!/usr/bin/env bash

#SBATCH --job-name=test
#SBATCH --partition=epyc
#SBATCH --mail-type=END,INVALID_DEPEND
#SBATCH --mail-user=noreply@uni-a.de
#SBATCH --time=1-0

# Request memory per CPU
#SBATCH --mem-per-cpu=1G
# Request n CPUs for your task.
#SBATCH --cpus-per-task=n

# set number of OpenMP threads
export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK

# Load application module here if necessary

# No need to pass number of tasks to srun
srun my_program

Setting the environment variable OMP_NUM_THREADS is essential. If omitted, your application might assume all cores of a node should be used which causes additional overhead and high load on a node.

For a multithreaded, single-node job make sure that the product of ntasks and cpus-per-task is equal to or less than the number of CPU-cores on a node, otherwise Slurm will refuse to accept your job submission.

Important

Only codes that have been explicitly written to use multiple threads will be able to take advantage of multiple CPU-cores. Using a value of cpus-per-task greater than 1 for a code that has not been parallelized will not improve its performance. Instead, doing so will waste resources and cause your next job submission to have a lower priority.

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