Sample Job Scripts

This page catalogues some typical job scripts for different methods of parallelizing code. These examples are not exhaustive and more examples can be found on individual software pages. However, these are typical of some of the most important styles of computation on an HPC system: Serial Computing, Shared Memory Parallelism, Distributed Memory Parallelism, Job-Level Parallelism and GPU Accelerated Computing.

Serial Computing

This script launches one process with one CPU core to run for 5 minutes with 1G of memory.

serial_job.slurm:

#!/bin/bash
#SBATCH --time=0-0:5
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem=1G

~/anaconda3/bin/python -c "print(3+5)"

Shared Memory Parallelism

This script starts one process with four CPU cores. In order for this to work, the OpenMP code arrayUpdate.c must be compiled with OpenMP support, e.g. gcc -fopenmp arrayUpdate.c

sharedmemory_job.slurm:

#!/bin/bash
#SBATCH --time=0-1:5:0
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --mem-per-cpu=1000M

export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
./arrayUpdate

Distributed Memory Parallelism

This example script launches four MPI processes, each with 1000 MB of memory.

distributedMemory_job.slurm:

#!/bin/bash
#SBATCH --time=1:00:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4               # number of MPI processes should match total ntasks
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1000M      # memory allocated for each cpu


mpiexec ./matrixProduct_program

Job-Level Parallelism

jobParallel_job.slurm:

#!/bin/bash
#SBATCH --time=1-0:0:0
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem=1G
#SBATCH --array=1-10

./recon-all $SLURM_ARRAY_TASK_ID

GPU Accelerated Computing

gpu_job.slurm:

#!/bin/bash
#SBATCH --time=12-0:0:0
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem=50000M
#SBATCH --gres=gpu:1

python tensorflow_example.py