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Job Submission

This section describes the interaction with the resource manager. In particular the job submission process with SLURM is described.

A cluster is a set of connected computers that work together to solve computational tasks (user jobs) and presents itself to the user as a single system. For the resources of a cluster (e.g. CPUs, GPUs, memory) to be used efficiently, a resource manager (also called workload manager or batch-queuing system) is vital. The resource manager of choice on UBELIX is SLURM. After submitting a job to the cluster, SLURM will schedule the job’s resource request by allocating resources to the job. If the requested resources are already available, the job can start immediately. Otherwise, the start of the job is delayed (pending) until enough resources are available. SLURM allows you to monitor active (pending, running) jobs and to retrieve statistics about finished jobs e.g. (peak CPU usage).

Sneak Peek: A simple Python example

Create sbmission script, allocating 8CPUs, 8GB memory for 1hour:

#SBATCH --job-name="Simple Python example"
#SBATCH --time=01:00:00
#SBATCH --mem-per-cpu=1G
#SBATCH --cpus-per-task=8

# Your code below this line
module load Anaconda3
eval "$(conda shell.bash hook)"

Submit the job script:

Submitted batch job 30215045

See below for more detailed examples and background information.

Resource Allocation

Every job submission starts with a resources allocation (nodes, cores, memory). An allocation is valid for a specific amount of time, and can be created using the salloc, sbatch or srun commands. Whereas salloc and sbatch only create resource allocations, srun launches parallel tasks within such a resource allocation, or implicitly creates an allocation if not started within one. The usual procedure is to combine resource requests and task execution (job steps) in a single batch script (job script) and then submit the script using the sbatch command.


The sbatch command is used to submit a job script for later execution. It is the most common way to submit a job to the cluster due to its reusability. Slurm options are usually embedded in a job script prefixed by #SBATCH directives. Slurm options specified as command line options overwrite corresponding options embedded in the job script


sbatch [options] script [args...]

Job Script

Usually a job script consists of two parts.

  • Slurm-specific options used by the scheduler to manage the resources (e.g. memory) and configure the job environment
  • Job-specific commands

The job script acts as a wrapper for your actual job. Command-line options can still be used to overwrite embedded options. Although you can specify all Slurm options on the command-line, we encourage you, for clarity and reusability, to embed Slurm options in the job script

Default values

Some options have default values if not specified,e.g. the --mem-per-cpu option has a global default value of 2048MB. Pleasesee the table below for other default values.


--job-name Specify a job name --job-name="Simple Matlab"
--time Expected runtime of the job. Format: dd-hh:mm:ss --time=12:00:00
--ntasks Number of tasks (processes). Used for MPI jobs that may run distributed on multiple compute nodes --ntasks=4
--nodes Request a certain number of nodes --nodes=2
--ntasks-per-node Specifies how many tasks will run on each allocated node. Meant to be used with --nodes. If used with the --ntasks option, the --ntasks option will take precedence and the --ntasks-per-node will be treated as a maximum count of tasks per node. --ntasks-per-node=2
--cpus-per-task Number of CPUs per task (threads). Used for shared memory jobs that run locally on a single compute node. Default is 1 --cpus-per-task=4
--mem-per-cpu Minimum memory required per allocated CPU in megabytes. Different units can be specified using the suffix [K|M|G]. Default 2048 MB --mem-per-cpu=2G
--output Redirect standard output. All directories specified in the path must exist before the job starts! By default stderr and stdout are merged into a file slurm-%j.out, where %j is the job allocation number. --output=myCal_%j.out
--error Redirect standard error. All directories specified in the path must exist before the job starts! By default stderr and stdout are merged into a file slurm-%j.out, where %j is the job allocation number. --output=myCal_%j.err
--partition Select a different partition with different hardware. See Partition/QoS page. Default: epyc2 --partition=bdw
--qos Specify “Quality of Service”. This can be used to change job limits, e.g. for long jobs or short jobs with large resources. See Partition/QoS page
--tmp Specify the amount of disk space that must be available on the compute node(s). The local scratch space for the job is referenced by the variable $TMPDIR. Default units are megabytes. Different units can be specified using the suffix [K|M|G|T]. --tmp=8G
--mail-user Mail address to contact job owner.
Must be a valid email address, if used!
--mail-type When to notify a job owner: none, all, begin, end, fail, requeue, array_tasks --mail-type=end,fail
--array Submit an array job. Specify the used indices and use “%” to specify the max number of tasks allowed to run concurrently. --array=1,4,16-32:4
--chdir Set the working directory of the batch script to directory before it is executed. The path can be specified as full path or relative path to the directory where the command is executed. Environment variables are not supported. --chdir=subdir1
--dependency Defer the start of this job until the specified dependencies have been satisfied. See man sbatch for a description of all valid dependency types --dependency=afterok:11908
--immediate Only submit the job if all requested resources are immediately available
--exclusive Use the compute node(s) exclusively, i.e. do not share nodes with other jobs. CAUTION: Only use this option if you are an experienced user, and you really understand the implications of this feature. If used improperly, the use of this option can lead to a massive waste of computational resources
--test-only Validate the batch script and return the estimated start time considering the current cluster state
--account Specifies account to charge. Please use Workspace module to select Workspace account. Regular users don’t need to specify this option.


The salloc command is used to allocate resources (e.g. nodes), possibly with a set of constraints (e.g. number of processor per node) for later utilization. It is typically used to allocate resources and spawn a shell, in which the srun command is used to launch parallel tasks.


salloc [options] [<command> [args...]]


bash$ salloc -N 2 -t 10
salloc: Granted job allocation 247
bash$ module load foss
bash$ srun ./mpi_hello_world
Hello, World.  I am 1 of 2 running on
Hello, World.  I am 0 of 2 running on
bash$ exit
salloc: Relinquishing job allocation 247


The srun command creates job steps. One or multiple srun invocations are usually used from within an existing resource allocation. Thereby, a job step can utilize all resources allocated to the job, or utilize only a subset of the resource allocation. Multiple job steps can run sequentially in the order defined in the batch script or run in parallel, but can together never utilize more resources than provided by the allocation.


Do not submit a job script using srun directly. Always create an allocation with salloc or embed it in a script submitted with sbatch.


srun [options] executable [args...]

When do I use srun in my job script?

Use srun in your job script for all main executables, especially if these are:

  • MPI applications
  • multiple job tasks (serial or parallel jobs) simultaneously within an allocation

Example Run MPI task:

#SBATCH --job-name="Open MPI example"
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=20
#SBATCH --mem-per-cpu=2G
#SBATCH --time=06:00:00

# Your code below this line
module load foss
srun ./mpi_app.exe

Run two jobs simultaneously:

#SBATCH --job-name="Open MPI example"
#SBATCH --ntasks=2
#SBATCH --cpus-per-task=4

# Your code below this line
# run 2 threaded applications side-by-side
srun --tasks=1 --cpus-per-task=4 ./app1 inp1.dat &
srun --tasks=1 --cpus-per-task=4 ./app2 inp2.dat &
# wait: Wait for both background commands to finish. This is important when running bash commands in the background (using &)! Otherwise, the job ends immediately. 

Please run series of similar tasks as job array. See Array Jobs

Requesting a Partition / QoS (Queue)

Per default jobs are submitted to the epyc2 partition and the default QoS job_epyc2. The partition option can be used to request different hardware, e.b. gpu partition. And the QoS can be used to run in a specific queue, e.g. job_gpu_debug:

#SBATCH --partition=gpu
#SBATCH --qos=job_gpu_debug

See Partitions / QoS for a list of available partitions and QoS and its specifications.


By default a user has a personal account. When belonging to a Workspace you can also submit with the Workspace account. If necessary, the account to use can be selected by the --account option. For now this is usually not required.

If a wrong account/partition combination is requested, you will experience the following error message:

sbatch: error: Batch job submission failed: Invalid account or account/partition combination specified

Parallel Jobs

A parallel job requires multiple compute cores. These could be within one node or across the machine in multiple nodes. We distinguish following types:

  • shared memory jobs: SMP, parallel jobs that run on a single compute node. The executable is called once. Within the execution (OMP) threads are spawned and merged.

    #SBATCH --ntasks=1           # default value
    #SBATCH --cpus-per-task=4

  • MPI jobs: parallel jobs that may be distributed over multiple compute nodes. Each task starts the executable. Within the application different workflows need to be defined for the different tasks. The tasks can communicate using Message Passing Interface (MPI). A job with 40 tasks:

    #SBATCH --nodes=2
    #SBATCH --ntasks-per-node=20

  • hybrid: jobs using a combination of MPI tasks and (OMP) threads.

    #SBATCH --nodes=2
    #SBATCH --ntasks-per-node=5
    #SBATCH --cpus-per-task=4


The requested node,task, and CPU resources must match! For example, you cannot request one node (--nodes=1) and more tasks (--ntasks-per-node) than CPU cores are available on a single node in the partition. In such a case you will experience an error message:

sbatch: error: Batch job submission failed: Requested node configuration is not available.

mpiexec vs mpirun vs srun

Parallel MPI applications need a launcher to setup the environment. We strongly suggest to use srun instead of mpirun or mpiexec.

Environment Variables

Slurm sets various environment variables available in the context of the job script. Some are set based on the requested resources for the job.

Environment Variable Set By Option Description
SLURM_JOB_NAME --job-name Name of the job
SLURM_ARRAY_TASK_ID --array ID of the current array task
SLURM_ARRAY_TASK_MAX --array Job array’s maximum ID (index) number
SLURM_ARRAY_TASK_MIN --array Job array’s minimum ID (index) number
SLURM_ARRAY_TASK_STEP --array Job array’s index step size
SLURM_NTASKS --ntasks Same as -n, --ntasks
SLURM_NTASKS_PER_NODE --ntasks-per-node Number of tasks requested per node. Only set if the --ntasks-per-node option is specified
SLURM_CPUS_PER_TASK --cpus-per-task Number of cpus requested per task. Only set if the --cpus-per-task option is specified
TMPDIR References the disk space for the job on the local scratch

For the full list, see man sbatch

Job Examples

Sequential Job

Running a serial job with email notification in case of error (1 task is default value):

#SBATCH --mail-type=end,fail
#SBATCH --job-name="Serial Job"
#SBATCH --time=00:10:00

# Your code below this line
echo "I'm on host: $HOSTNAME"

Parallel Jobs

Shared Memory Jobs (e.g. OpenMP)

SMP parallelization is based upon dynamically created threads (fork and join) that share memory on a single node. The key request is --cpus-per-task. To run N threads in parallel, we request N CPUs on the node (--cpus-per-task=N).

#SBATCH --mail-type=end,fail
#SBATCH --job-name="SMP Job"
#SBATCH --mem-per-cpu=2G
#SBATCH --cpus-per-task=16
#SBATCH --time=01:00:00

# Your code below this line
srun ./my_binary

MPI Jobs (e.g. Open MPI)

MPI parallelization is based upon processes (local or distributed) that communicate by passing messages. Since they don’t rely on shared memory those processes can be distributed among several compute nodes. Use the option --ntasks to request a certain number of tasks (processes) that can be distributed over multiple nodes:

#SBATCH --mail-type=end
#SBATCH --job-name="MPI Job"
#SBATCH --mem-per-cpu=2G
#SBATCH --ntasks=8
#SBATCH --time=04:00:00

# Your code below this line
# First set the environment for using Open MPI
module load foss
srun ./my_binary

On the ‘bdw’ partition you must use all CPUs provided by a node (20 CPUs). For example to run an OMPI job on 80 CPUs, do:

#SBATCH --mail-type=end,fail
#SBATCH --job-name="MPI Job"
#SBATCH --mem-per-cpu=2G
#SBATCH --nodes=4     ## or --ntasks=80
#SBATCH --ntasks-per-node=20
#SBATCH --time=12:00:00

# Your code below this line
module load foss
srun ./my_binary

Performance considerations

Job Throughput

Job series

Submit series of jobs (collection of similar jobs) as array jobs instead of one by one. This is crucial for backfilling performance and hence job throughput. instead of submitting the same job repeatedly. See Array jobs

It is crucial to specify a more or less accurate runtime for your job.
Requesting too little will result in job abortion, while requesting too much will have a negative impact on job start time and job throughput: Firstly, jobs with a shorter runtime have a greater chance to benefit from being backfilled between long running jobs and may therefore start earlier if resources are scarce. Secondly, a short running job may still start when a scheduled downtime is getting closer while long running jobs won’t start because they are not guaranteed to finish before the start of the downtime.

It is crucial to request the correct amount of cores for your job.
Requesting cores that your job cannot utilize is a waste of resources that could otherwise be allocated to other jobs. Hence, jobs that theoretically could run have to wait for the resources to become available. For potential consequences of requesting too less cores on job performance, see below.

It is crucial to request the correct amount of memory for your job.
Requesting too little memory will result in job abortion. Requesting too much memory is a waste of resources that could otherwise be allocated to other jobs.

Job Performance/Runtime

It is crucial to request the correct amount of cores for your job.
For parallel jobs (shared memory, MPI, hybrid) requesting less cores than processes/threads are spawned by the job will lead to potentially overbooked compute nodes. This is because your job will nevertheless spawn the required number of processes/threads (use a certain number of cores) while to the scheduler it appears that some of the utilized resources are still available, and thus the scheduler will allocate those resources to other jobs. Although under certain circumstances it might make sense to share cores among multiple processes/threads, the above reasoning should be considered as a general guideline, especially for inexperienced user.

Advanced settings


By default the environment from the session, where the job is submitted is forwarded into the job environment. As a result all loaded modules and environment variables present during submit time are also present during run time.

To start from a clean environment the environment forwarding can be prevented. Therefore, we have to keep in mind the 2 stages. First, after submission the job script is launched on a compute node. Second, the parallel tasks are launched using srun. We want to preserve the environment forwarding from within the job script to the parallel tasks. Using just the option sbatch --export=none or the environment variable SBATCH_EXPORT=NONE will prevent the forwarding in both cases. Therewith, issues may occur, like dynamically linked binaries do not find libraries (e.g. error while loading shared libraries: cannot open shared object file: No such file or directory).

Thus we suggest to set:

This will provide you with a clean environment during job launch and forward the environment set in the job script to the parallel tasks. This is necessary to have e.g. LD_LIBRARY_PATH set correctly for dynamically linked binaries.