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Running Batch Jobs#

Job Scheduling and Management#

Batch jobs are run by submitting a job script to the scheduler with the sbatch command. The job script contains the commands needed to set up your environment and run your application. (This is an "unattended" run, with results written to a file for later access.)

Once submitted, the scheduler will insert your job script into the queue to be run at some point in the future, based on priority and how many jobs are in the queue currently.

Priority factors vary on a cluster-by-cluster basis, but typically include a "fairshare" value based on the resources assigned to the allocation, as well as weighting by the job's age, partition, resources (e.g. node count) and/or Quality of Service (qos) factor. Please see the Monitoring and Control commands page for more information on checking your job's priority. The Systems documentation for each cluster will also have more information about the priority weighting, QOS factors, and any associated AU upcharges.

To submit batch jobs on an HPC system at NREL, the Slurm sbatch command should be used:

$ sbatch --account=<project-handle> <batch_script>

Sbatch scripts may be stored on or run from any file system (/home or /projects, for example), as they are typically fairly lightweight shell scripts. However, on most HPC systems it's generally a good idea to have your executables, conda environments, other software that your sbatch script executes stored in a /projects directory. Your input and output files should typically be read from and/or written to either /projects or /scratch directories, as well. Please see the appropriate Systems page for more information specific to the filesystems on the NREL-hosted cluster you're working on to maximize I/O performance.

Arguments to sbatch may be used to specify resource limits such as job duration (referred to as "walltime"), number of nodes, etc., as well as what hardware features you want your job to run with. These can also be supplied within the script itself by placing #SBATCH comment directives within the file.

Required Flags#

Resources for your job are requested from the scheduler either through command line flags to sbatch, or directly inside your script with an #SBATCH directive. All jobs require the following two flags to specify an allocation ("account") to charge the compute time to, and a maximum duration:

Parameter Flag Example Explanation
Project handle --account, -A --account=<handle> or -A <handle> Project handles are provided by HPC Operations at the beginning of an allocation cycle.
Maximum Job Duration (walltime) --time, -t --time=1-12:05:50
(1 day, 12 hours, 5 minutes, and 50 seconds)
-t5 (5 minutes)
Recognized Time Formats:

Resource Request Descriptions#

Specific resources may be requested from the scheduler to help the scheduler assign appropriate number and type of node or nodes to your job:

Parameter Flag Example Explanation
Nodes, Tasks, MPI Ranks --nodes or -N
--ntasks or -n
if ntasks is specified, it is important to indicate the number of nodes request as well. This helps with scheduling jobs on the fewest possible Ecells (racks) required for the job.

The maximum number of tasks that can be assigned per node is equal to the CPU (core) count of the node.
Memory --mem
--mem=50000 Memory per node
memory per task/MPI rank
Local disk (/tmp/scratch) --tmp --tmp=10TB
Request /tmp/scratch space in megabytes (default), GB, or TB.
GPUs --gres:gpu --gres:gpu:2 Requests 2 GPUs. See system information for total number of GPUs.

Job Management and Output#

Command and control and monitoring customization are also available:

Parameter Flag Example Explanation
High priority --qos --qos=high High-priority jobs will take precedence in the queue. Note: There is an AU penalty of 2X for high-priority jobs.
Dependencies --dependency --dependency=<condition>:<job_id>


You can submit jobs that will wait until a condition is met before running.


After the listed jobs have started
After the listed jobs have finished
After the listed jobs have failed
After the listed jobs return exit code 0
After all existing jobs with the same name and user have ended
Job Name --job-name --job-name=myjob A short, descriptive job name for easier identification in the queue.
Email notifications --mail-user
Slurm will send updates on job status change. Type can be specified with --mail-type as BEGIN, END, FAIL, or ALL.
Output --output


Defaults to slurm-<jobid>.out

Defaults to slurm-<jobid>.out (same file as stdout)

stdout and stderr will be written to the same file unless specified otherwise

Commonly Used Slurm Environment Variables#

You may use these environment variables in your sbatch scripts to help control or monitor various aspects of your job directly within the script, as well:

Parameter Semantic Value Sample Value
$LOCAL_SCRATCH Absolute directory path for local-only disk space per node. This should always be /tmp/scratch for compute nodes with local disk. /tmp/scratch
$TMPDIR Path for temporary directory for scratch space. Uses local storage on compute nodes with local disk, and RAM on those without. /tmp/scratch/<JOBID> (default value on Kestrel)
$SLURM_CLUSTER_NAME The cluster name as per the master configuration in Slurm. Identical to $NREL_CLUSTER. kestrel, eagle,swift
$SLURM_CPUS_ON_NODE Quantity of CPUs per compute node. 104
$SLURMD_NODENAME Slurm name of the node on which the variable is evaluated. Matches hostname. r4i2n3
$SLURMD_JOB_ACCOUNT The Slurm account used to submit the job. Matches the project handle. csc000
$SLURM_JOB_CPUS_PER_NODE Contains value of --cpus-per-node, if specified. Should be equal or less than $SLURM_CPUS_ON_NODE. 104
$SLURM_JOBID or $SLURM_JOB_ID Job ID assigned to the job. 521837
$SLURM_JOB_NAME The assigned name of the job, or the command run if no name was assigned. bash
$SLURM_JOB_NODELIST or $SLURM_NODELIST Hostnames of all nodes assigned to the job, in Slurm syntax. r4i2n[1,3-6]
$SLURM_JOB_NUM_NODES or $SLURM_NNODES Quantity of nodes assigned to the job. 5
$SLURM_JOB_PARTITION The scheduler partition the job is assigned to. short
$SLURM_JOB_QOS The Quality of Service the job is assigned to. high
$SLURM_NODEID A unique index value for each node of the job, ranging from 0 to $SLURM_NNODES. 0
$SLURM_STEP_ID or $SLURM_STEPID Within a job, sequential srun commands are called "steps". Each srun increments this variable, giving each step a unique index nmber. This may be helpful for debugging, when seeking which step a job fails at. 0
$SLURM_STEP_NODELIST Within a job, srun calls can contain differing specifications of how many nodes should be used for the step. If your job requests 5 total nodes and you used srun --nodes=3, this variable would contain the list of the 3 nodes that participated in this job step. r4i2n[2-4]
$SLURM_STEP_NUM_NODES Returns the quantity of nodes requested for the job step (see entry on $SLURM_STEP_NODELIST.) 3
$SLURM_STEP_NUM_TASKS Returns the quantity of tasks requested to be executed in the job step. Defaults to the task quantity of the job request. 1
$SLURM_STEP_TASKS_PER_NODE Contains the value specified by --tasks-per-node in the job step. Defaults to the tasks-per-node of the job request. 1
$SLURM_SUBMIT_DIR Contains the absolute path of the directory the job was submitted from. /projects/csc000
$SLURM_SUBMIT_HOST The hostname of the system from which the job was submitted. Should always be a login node. el1
$SLURM_TASKS_PER_NODE Contained the value specified by --tasks-per-node in the job request. 1

Example SBATCH Script Walkthrough#

Many examples of sbatch scripts are available in the HPC Repository Slurm Directory on Github.

Here's a basic template job script to get started, followed by a breakdown of the individual components of the script. This script may be adapted to any HPC system with minor modifications. Copy it into a file on the cluster, make any necessary changes, and save it as a file, e.g. "".

#SBATCH --account=<allocation>
#SBATCH --time=4:00:00
#SBATCH --job-name=job
#SBATCH --output=job_output_filename.%j.out  # %j will be replaced with the job ID

module load myprogram

Script Details#

Here is a section-by-section breakdown of the sample sbatch script, to help you begin writing your own.

Script Begin#


This denotes the start of the script, and that it is written in BASH shell language, the most common Linux environment.

SBATCH Directives#

#SBATCH --account=<allocation>
#SBATCH --time=4:00:00
#SBATCH --job-name=job
#SBATCH --output=job_output_filename.%j.out  # %j will be replaced with the job ID
Generalized form:

#SBATCH --<command>=<value>

Command flags to the sbatch program are given via #SBATCH directives in the sbatch script. There are many flags available that can affect your job, listed in the previous section. Please see the official Slurm documentation on sbatch for a complete list, or view the man page on a login node with man sbatch.

Sbatch directives must be at the beginning of your sbatch script. Once a line with any other non-directive content is detected, Slurm will no longer parse further directives.

Note that sbatch flags do not need to be issued via directives inside the script. They can also be issued via the commandline when submitting the job. Flags issued via commandline will supercede directives issued inside the script. For example:

sbatch --account=csc000 --time=60 --partition=debug

Job Instructions#

After the sbatch directive block, you may then begin executing your job. The syntax is normal BASH shell scripting. You may load system modules for software, load virtual environments, define environment variables, and execute your software to perform work.

In the simplest form, your sbatch script should load your software module(s) required, and then execute your program.

module load myprogram

module load myprogram

You may also use more advanced bash scripting as a part of your sbatch script, e.g. to set up environments, manage your input and output files, and so on.

More system-specific information about Slurm partitions, node counts, memory limits, and other details can be found under the appropriate Systems page.

You may also visit the "master" main branch of the Github repository for downloadable examples, or to contribute your own.