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.
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: Setting the environment variable 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. Use the "snodes" command and look at the "CPUS" column to see the CPU-cores per node information. 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.Pure OpenMP Jobs (n CPUs)
#!/usr/bin/env bash
#SBATCH --job-name=test
#SBATCH --partition=epyc
#SBATCH --mail-type=END,INVALID_DEPEND
#SBATCH --mail-user=<e-mail address>
#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
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.
