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Job Scripts

Serial Single Threaded

This example illustrates a job script designed to run a simple single-threaded processes on a single compute node:

{{{#!/bin/bash # ====Sample Job Script=== #SBATCH --job-name=mySerialjob #SBATCH --nodes=1 #SBATCH --ntasks-per-node=1 #SBATCH --cpus-per-task=1 #SBATCH --time=0-00:20:00 #SBATCH --mem=3102


module load someApp someApp }}}


A single process run only requires 1 node, as well as 1 cpu core and a single task. These are reflected in the example script. We change to the same directory from where we submitted the job ( ${SLURM_SUBMIT_DIR}) to produce our output. Then we load the module "someApp" and execute it.

Note that ${SLURM_SUBMIT_DIR} is one of many environmental variables available from within a SLURM job script. For a comprehensive list, please refer to the SLURM documentation.

Multi-Threaded Single Node

In this example we are running an application capable of utilizing multiple process threads on a single node (BLAST):

{{{#!/bin/bash # ====Sample Job Script=== #SBATCH --job-name=myBLASTjob #SBATCH --nodes=1 #SBATCH --ntasks-per-node=1 #SBATCH --cpus-per-task=8 #SBATCH --time=0-01:00:00 #SBATCH --mem=3102


module load BLAST blastn --num_threads 8 <...> }}}


In this case we still have a single task (our blastn run) but we require 8 cpu cores to accommodate the 8 threads we've specified on the command line. The ellipses between the angle brackets represents the balance of our command line arguments.

Multiple Serial Jobs

Here we demonstrate it is possible to run multiple copies of the same application, and leverage SLURM's "srun" command to distribute tasks on multiple nodes:

{{{#!/bin/bash # ====Sample Job Script=== #SBATCH --nodes=2 #SBATCH --ntasks-per-node=2 #SBATCH --cpus-per-task=1 #SBATCH --time=0-01:00:00 #SBATCH --mem=3102

module load someApp srun -n 2 python myScript.py & srun -n 2 someApp & wait }}}


We specify 2 nodes and 2 tasks per node (total 4 tasks). The "srun" command is used to direct that 2 copies of each application should be run. srun works with SLURM to launch and schedule each task across our assigned nodes. The ampersand (&) causes each task to be run "in the background" so that all tasks may be launched in parallel and are not blocked waiting for other tasks to complete. The "wait" directive tells SLURM to wait until all background tasks are completed.

MPI Jobs

This is an example of a job script that runs a single MPI application across multiple nodes with distributed memory. It is recommended to use "srun" instead of "mpirun":

{{{#!/bin/bash # ====Sample Job Script=== #SBATCH --nodes=2 #SBATCH --ntasks-per-node=14 #SBATCH --ntasks=28 #SBATCH --cpus-per-task=1 #SBATCH --mem-per-cpu=1G #SBATCH --time=0-10:00:00

module load OpenMPI

srun ./my_mpi_app }}}


Two nodes are assigned with 14 tasks per node (28 tasks total). One GB of RAM is allocated per CPU and srun is used to launch our MPI-based application.

Interactive Jobs

It is possible to schedule an interactive job on the cluster. This can be accomplished using "srun" and specifying resource parameters on the command line:

srun -N 1 -c 1 -t 30:00 --pty /bin/bash --login


Here 1 node is specified as is 1 core, with a walltime of 30 minutes. The balance of the command gives us a bash login shell that will be scheduled by SLURM on one of the compute nodes.

Job Arrays

Job arrays are a convenient way to perform the same set of procedures or tasks on multiple data sets without having to launch more than one job. This reduces the number of job scripts required, and allows jobs to run in parallel with a single script. In the example below, we are executing the same process on 4 different input files:

{{{#!/bin/bash # ====Sample Job Script=== #SBATCH --job-name=myArrayest #SBATCH --nodes=1 #SBATCH --ntasks-per-node=1 #SBATCH --cpus-per-task #SBATCH --time=0-10:00:00 #SBATCH --array=1-4

file=$(awk "NR==${SLURM_ARRAY_TASK_ID}" file_list.txt) python /home/someUser/myscript.py $file > myoutput_${SLURM_ARRAY_TASK_ID}.out }}}


  1. The line "#SBATCH --array=1-4" specified we are running 4 tasks, numbered 1-4
  2. The line beginning "file=" uses the scripting language "awk" to read the line number corresponding to the SLURM_ARRAY_TASK_ID (1-4) from the file "file_list.txt" which is contained in the working directory
  3. The python script "myscript.py" operates on the value returned for "$file" (the filename) and to store the output in a file named "myoutput_#.out" where "#" corresponds to the job array ID for the SLURM task.