Slurm @ UAntwerp

This page covers the more basic Slurm use, including starting jobs, basic job management and some templates for job scripts for various scenarios. It is the minimum a user should master. A second page describes more advanced use of Slurm.

What and why?

Since the start of the VSC, Torque and Moab were used as the resource manager and scheduler respectively. The resource manager is responsible for keeping track of resources and making sure jobs use the resources allocated to them. The scheduler is the piece of software that prioritises jobs that are waiting in the queue and decides which job can start with which resources. It is clear that both have to work together very closely. Torque and Moab were developed and supported by Adaptive Computing. This company was acquired by ALA Services Technology Companies. Since then the software isn’t well supported anymore, resulting in problems to keep it running on our systems.

Therefore, the decision was taken to transfer to a different resource manager and scheduler software. Slurm Workload Manager was chosen due to its wide use in academic supercomputer centres. We’ve been preparing for this switch for over two years now by stressing in the introductory courses those features of Torque and Moab that resemble Slurm features the most.

Slurm Workload Manager is also used on the clusters at UGent (but with a wrapper that still accepts Torque job scripts with some limitations) and will also be the scheduler on Hortense, the successor of the BrENIAC Tier-1 system.

Historically, Slurm was an acronym of Simple Linux Utility for Resource Management. The development started around 2002 at Lawrence Livermore National Lab as a resource manager for Linux clusters. Slurm has always had a very modular architecture. From 2008 on increasingly sophisticated scheduling plugins were added to Slurm. Nowadays it is used on some of the largest systems in the world. Slurm is completely open source though commercial support can be obtained from SchedMD, a spin-off company of the Slurm development.

Slurm concepts

  • Nodes: On the UAntwerp clusters (and most other clusters) a node is the largest part of the cluster running a single operating system image, and hence capable of supporting a shared memory program. Nodes are connected with each other through an interconnect, and communication between nodes is done via message passing.

  • Core: A core is a physical core in a system.

  • CPU: A CPU is a virtual core in a system, in other words, a hardware thread in a system with hyperthreading/SMT enabled. On a system with hyperthreading/SMT disabled, virtual cores are just physical cores.

  • Partition: Groups of nodes with limits and access controls, basically the equivalent of a queue in Torque. A node can be part of multiple partitions.

  • Job: A resource allocation request.

  • Job step: A set of (possibly parallel) tasks within a job. A job can consist of just a single job step or can contain multiple job steps which may use all or just a part of the resource allocation of a job and can run sequentially or in parallel (or a mix of that). The job script itself is a special job step, called the batch job step, but additional job steps can be created (e.g., for running a parallel MPI application).

  • Task: A task is executed within a job step and essentially corresponds to a Linux process: a single- or multithreaded process, or a single rank within a MPI process. Specifying the number of tasks one wants to run simultaneously and the number of cores per task is a very convenient way to request resources to Slurm as afterwards starting a MPI or hybrid MPI/OpenMP program using the srun command is very easy.

Slurm commands

Submitting a job script: sbatch

Slurm sbatch manual page on the web

The sbatch is the command in Slurm to submit a job script. A job script first contains a list of resources and other instructions to Slurm, and this is followed by a set of commands that will be executed on the first node of the job. When the submission succeeds, sbatch will print a message containing the unique job ID for the job.

Resource specifications and other instructions can be specified in three different ways: command line options, environment variables, and #SBATCH lines in the job script.

  • Slurm sbatch has a lot of command line options. We will only list the most important command line options below. Command line options of Slurm take precedence over environment variables and #SBATCH lines in the job script.

  • Some command line options can also be passed to sbatch through environment variables instead. A list of those can be found in the sbatch manual page. The name of those variables starts with SBATCH_ and the remaining part is derived from the matching command line option. However, be careful when using those and hiding them in .bashrc or .bash_profile as they are easily forgotten yet have a higher priority than those on SBATCH lines which is the most used mechanism to specify resources etc.

  • All command line options can also be passed in #SBATCH lines in the job script. These lines should follow immediately below the shebang in the first block of comment lines (lines that start with #) as otherwise they will be ignored by Slurm.

Note that all sbatch command line options should be specified before the name of the job script. All command line parameters specified after the name of the job script will be passed as command line arguments to the job script when it executes.

Requesting compute resources

Slurm supports several ways to request CPU cores and/or GPUs for a job.

The easiest way to request CPU cores is by following the “task”-idea of Slurm and specifying the number of parallel tasks and cores per task that you need. By specifying resources this way, it is very easy afterwards to start OpenMP, MPI and hybrid MPI/OpenMP programs in the right configuration.

  • The number of tasks is specified by --ntasks=<number of tasks> or -n <number of tasks>. The =-sign in the long option format can be replaced by a space, and in the short form (-n) the space between the flag and the value form can also be omitted (in effect, this holds for all options).

  • The number of CPUs (hardware threads) per task is specified by --cpus-per-task=<value> or -c <value>. On the UAntwerp clusters, CPUs are physical cores (since hyperthreading is disabled). For each task, all of the CPUs for that task are allocated on a single node. When using multiple nodes, the allocated CPUs for all tasks are distributed equally over all the nodes (except possibly for the last node).

Make sure to request a valid combination of tasks and/or CPUs per task. Otherwise, your job can be rejected or it could end up in the partition queue but it will never start (in that case, check the reason code, as explained later in this document in the section on checking the queue).

If set, the Slurm controller will set the corresponding variables, respectively SLURM_NTASKS and SLURM_CPUS_PER_TASK in the environment of the running job.

If not set, the default values of 1 task and 1 CPU are used.

Requesting memory

Slurm jobs can also request an amount of RAM space (resident memory). In case of the UAntwerp clusters, swapping for jobs is disabled since the nodes don’t have drives suitable for the load caused by swapping and since swapping is extremely detrimental to the performance of the cluster. Therefore, swap space cannot be requested.

Slurm has various ways to request memory. Unfortunately, there is currently no way to request memory per task. The preferred method for requesting memory in Slurm on the UAntwerp clusters is to specify the amount of memory per CPU (per core on the UAntwerp clusters): --mem-per-cpu=<amount><unit> (e.g., --mem-per-cpu=1g). The amount is an integer, <unit> can be either k for kilobytes, m for megabyte or g for gigabyte.

The job will be rejected if the final amount of memory requested cannot be satisfied. This could happen if --mem-per-cpu times the number of CPUs on a node is greater than the memory on that node that is available for job allocations. Note that on the UAntwerp clusters, the memory available for job allocations is somewhat less than the total memory installed on a node (to keep some amount of memory for the OS and file system buffers).

If not set, a default value will be used, equal to the total memory available for job allocations of that node divided by the number of CPUs.

The amount of available memory per CPU is available via the variable SLURM_MEM_PER_CPU as an integer with megabytes as unit in the environment of the running job.

Requesting wall time

The requested compute time is specified using --time=<time> or -t <time>. <time> is specified in mm (minutes), mm:ss (minutes and seconds), hh:mm:ss (hours, minutes and seconds), d-hh (days and hours), d-hh:mm (days, hours and minutes) or d-hh:mm:ss (days, hours, minutes and seconds) format. The - is not a typo!

If not set, a default wall time of 1 hour will be assigned.

Specifying a partition

Slurm jobs can be submitted to a certain partition of compute nodes. Indicating the kind of job in this manner imposes some additional restrictions on resources and time, but may let the job start sooner. The partition can be specified using --partition=<partition> or -p <partition>.

If not set, the default partition will be used.

The name of partition is available in the variable SLURM_JOB_PARTITION in the environment of the running job.

Specifying a job name

The default name of a job is the name of the job script. The name can however be changed using --job-name=<name> or -J <name>.

The name of the job is available in the variable SLURM_JOB_NAME in the environment of the running job.

Redirecting stdout and stderr

By default, Slurm redirects both stdout and stderr to the same file, named slurm-<jobid>.out. There are two flags to sbatch to change this behaviour:

  • --output=<output file> or -o <output file> will redirect all output to the file specified by <output file> rather than the default.

  • --error=<error file> or -e <error file> will redirect output sent to stderr to the file specified by <error file>. Output sent to stdout is still sent to the default file, unless --output is also used.


  • No --output and no --error: stdout and stderr are both sent to the default output file slurm-<jobid>.out.

  • --output specified but no --error: stdout and stderr are both sent to the file pointed to by --output.

  • No --output, but --error specified: stdout is redirected to the default output file slurm-<jobid>.out while stderr is redirected to the file pointed to by --error.

  • Both --output and --error are specified: stdout is redirected to the file pointed to by --output and stderr is redirected to the file pointed to by --error.

The file name can (and usually will) be a template. It can contain replacement symbols preceded by a % that allow to use the job ID etc. in the name of the file to ensure unique file names. The most useful of such symbols is %j which will be replaced by the unique job ID. A full list of replacement symbols can be found in the sbatch manual page.

Sending mail at specific events

Slurm can send mail when a job starts, fails or ends normally, and on a number of other occasions. Two flags influence this behaviour:

  • --mail-type=<type> specifies when mail should be sent. <type> is a comma-separated list of type values. Type values include BEGIN, END and FAIL to denote respectively the start of a job, end of a job and failure of a job, but there are many other options that can be found in the sbatch manual page.

  • --mail-user=<mail address> specifies to which mail address the mails should be sent. The default value is the mail address associated with the VSC-account of the submitting user.

If not set, no mail will be sent.

Specifying dependencies

Job dependencies can be used to defer the start of a job until the specified dependencies have been satisfied. They are very helpful when implementing a workflow consisting of steps with different requirements for each job in the workflow.

The basic way of specifying a job dependency is through --dependency=<type>:jobid:jobid,<type>:jobid:jobid etc. For (almost) each type one can specify one or more job IDs, and it is also possible to specify multiple types of dependencies.

Dependency type

What it does


Job can begin after all specified jobs have started (or are cancelled)


Job can begin after all specified jobs have terminated


Job can begin after the specified jobs has successfully completed


Job can begin after the specified jobs have failed


Job can start after all previously launched jobs with the same name and same user have ended. This can be useful to collate results after running a batch of related jobs.

The job environment

The Slurm sbatch command by default copies the environment in which the job script was submitted (at least, the environment seen by the sbatch command, so all exported variables and functions). This implies that, e.g., all modules that were loaded when you submitted the job script, will be loaded in your job environment. This poses a number of risks:

  • Some modules adapt their behaviour to the environment in which they were loaded. One important example are the modules that provide MPI on the cluster. When launched in a Slurm job environment, some environment variables are set to ensure maximal integration with Slurm. However, when loaded on the login nodes these variables are not set as otherwise running a MPI program as a regular program without mpirun (and launching just a single process) would fail. The latter is a problem for, e.g., Python when some module loads the Python MPI package.

  • You may be working in a different environment than the one you used the previous time you ran the job script, and as a consequence of this your job script that previously functioned well may now function differently.

  • Paths may be different on the login nodes and compute nodes. This can happen during OS upgrades of the cluster. These can often be done without downtime or interrupting work on the cluster, but that implies that some nodes will be running one version while other nodes will be running another version of the OS setup.

To alleviate these issues, we set a minimal environment for jobs by default. This means that, along with the SLURM_* variables, only the HOME, USER and TERM environment variables are exported to the job. The PATH environment variable is set to a minimum in the job environment. This implies that the desired software modules must be loaded in your job scripts for use during the execution of the job.

In case you would attempt to use the --export sbatch option to override this behaviour, we advise you to apply one of the following solutions to avoid accidental mistakes:

  • Clear the environment in your job script by reloading all modules that are needed and ensuring that all environment variables that you need are set. Cleaning the module environment can be done by calling

    module --force purge

    and then reloading the right calcua software stack module and application modules. This should be a common practice in all your job scripts.

  • Use one of the options --get-user-env or --export=NONE (either with the sbatch command or, preferably, as a #SBATCH line in the job script).

    The option --get-user-env will tell sbatch not to propagate the environment in which it executes, but to reconstruct the environment that you would get when you log on to the cluster. Though be aware that any environment variables already set in the environment will still take precedence over any environment variables in the user’s login environment. And there is also a difference with what you get when executing your .bash_profile script: The environment only contains exported variables and functions and no aliases or variables or functions that are not exported by .bash_profile.

    The option --export=NONE will only define SLURM_* variables from the user environment. When using this option, one must use an absolute path to the binary to be executed (which could then be used to further define the environment). When using this option, it is not possible to pass environment variables to the job script.

The Slurm controller also sets several SLURM_* variables in the environment of the running job. Some of these variables are only available if the corresponding option has been explicitly set, while other variables are always set (with default values filled in, if appropriate). Several of these variables are mentioned on our PBS-to-Slurm conversion tables page. A full list of all SLURM_* environments can be found in the sbatch manual page (in the section on “OUTPUT ENVIRONMENT VARIABLES”).

Starting multiple copies of a process in a job script: srun

Slurm srun manual page on the web

The srun command is used to start a new job step in a job script. The most common case is to start a parallel application. srun integrates well with major MPI implementations and can be used instead of mpirun or mpiexec to start a parallel MPI application. It then takes your resource requests and allocated resources into account and does a very good job of starting each MPI rank on the right set of cores even without having to use additional command line options. Further down this section/page there are a couple of examples that demonstrate the power of the srun command. The advantage of this way of working is that all processes run under the strict control of Slurm, ensuring that if something goes wrong, they are also cleaned up properly.

The srun command can also be used outside of a resource allocation, i.e., at the command line of the login nodes, outside a job script or an allocation obtained with salloc (see further in the text). It will then first create the resource allocation before executing the command given as an argument to srun. One useful case which we discuss further down in this text is to start an interactive session. Most of the command line options of sbatch to specify the properties of the allocation can also be used with srun.

Just as sbatch, srun will propagate the environment. When srun is used in a job script to start a parallel application, this is also very sensible and desired behaviour as it ensures the processes started with Slurm run in the right environment created by the job script.

Commands for managing jobs

Checking the queue: squeue

Slurm squeue manual page on the web

The Slurm command to list jobs in the queue is squeue.

The basic command without options will show basic information about all your jobs in the queue. There are a number of useful command line options though:

  • The --long or -l flag adds some additional information.

  • --format=<output format> or -o <output format> allows you to specify your custom output format that can show a lot more information. Likewise, --Format=<output format> or -O <output format> (with a capital first letter) can show even more information but with a longer syntax for the output format. See the squeue manual page for information on all format options.

  • It is possible to show that information for only one or a selection of your jobs by using --jobs=<job_id_list> or -j <job_id_list>, where <job_id_list> is a comma-separated list of job IDs.

The column “REASON” lists why a job is waiting for execution. It distinguishes between 30+ different reasons, way to much to discuss here, but some of the codes speak for themselves. The full list of reason codes can be found in the squeue manual page.

Kill/delete a job: scancel

Slurm scancel manual page on the web

The Slurm command to cancel a job is scancel. In most cases, it takes only a single argument, the unique identifier of the job to cancel.

For a job array (see below) it is also possible to cancel only some of the jobs in the array by specifying the array elements as follows:

scancel 20_[1-3]
scancel 20_4 20_6

The first command would kill jobs 1, 2 and 3 in the job array with job ID 20, the second command would kill jobs 4 and 6 of that job array.

As shown in the example above, a space separated list of multiple job IDs can also be specified, as well as a selection based on multiple filters, e.g., in which partition the job is running. Consult the scancel manual page for more information.

Getting more information on a running job: sstat

Slurm sstat manual page on the web

The sstat command displays information on running jobs pertaining to CPU, Task, Node, Resident Set Size (RSS) and Virtual Memory (VM) for all your running jobs. The jobs need to be explicitly mentioned using --jobs=<job_id_list> or -j <job_id_list> (where <job_id_list> is a comma-separated list of job IDs).

By default, it will only show information about the lowest job step running in a particular job unless --allsteps or -a is also specified. It is also possible to request information on a specific job step of a job by using <jobid.jobstep>, i.e., add the number of the job step to the job ID, separated by a dot.

To show additional information not shown by the default format, one can specify a specific format using the --format or identical --fields and -o flags. Check the sstat manual page for further information.

Getting information about a job after it finishes: sacct

Slurm sacct manual page on the web

Whereas sstat is used to show near real-time information for running jobs, sacct shows the information as it is kept by Slurm in the job accounting log/database. Hence it is particularly useful to show information about jobs that have finished already. It allows you to see how much CPU time, wall time, memory, etc. were used by the application.

By default, sacct shows the job ID, job name, partition name, account name, number of CPUs allocated to the job, the state of the job and the exit code of completed jobs. Several options can be used to modify the format:

  • --brief or -b shows only the job ID, state and exit ode.

  • --long or -l shows an overwhelming amount of information, probably more than you want to know as a regular user.

  • --format or -o can be used to specify your own output format. We refer to the sacct manual page for an overview of possible fields and how to construct the format string.

By default, sacct will show information about jobs that have been running since midnight. There are however a number of options to specify for which jobs you want to see information:

  • --jobs=<job_id_list> or -j <job_id_list> with a comma-separated list of job IDs (in the same format as for sstat) will only show information on those jobs (or job steps).

  • --starttime=<time> or -S <time> will show information about all jobs that have been running since the indicated start time. There are four possible formats for <time>: HH:MM[:SS] [AM|PM], MMDD[YY] or MM/DD[/YY] or MM.DD[.YY], MM/DD[/YY]-HH:MM[:SS] and YYYY-MM-DD[THH:MM[:SS]] (where [] denotes an optional part).

  • --endtime=<time> or -E <time> will show information about all jobs that have been running before the indicated end time. By combining a start time and end time it is possible to specify a window for the jobs.

For now, there is no reason to be concerned about the account name as we do not use accounting to control the amount of compute time a user can use.

Job types: Examples

Shared memory job

A shared memory program consists of a single task. The number of CPUs you would request for the task corresponds to the number of computational threads you want to use for the application. Keep in mind that a single task cannot span multiple nodes in the cluster.

Shared memory programs often need to be told how many threads they should use. Unfortunately, there is no standard way that works for all programs. Some programs require an environment variable to be set, others have a parameter in the input file and some interpreters (e.g., Matlab) require it to be set in the code being interpreted.

OpenMP is a popular technology for creating shared memory programs. Some OpenMP programs will read the number of threads from the input file and then set it using OpenMP library functions. But most OpenMP programs simply use the environment variable OMP_NUM_THREADS to determine the number of threads that should be used.

The following job script is an example of this. It assumes there is a program omp in the current directory compiled with the intel/2020a toolchain. It will be run on 10 cores.

#! /bin/bash
#SBATCH --job-name=OpenMP-demo
#SBATCH --ntasks=1 --cpus-per-task=10 --mem=2g
#SBATCH --time=05:00

# Build the environment
module --force purge
module load calcua/2020a
module load vsc-tutorial

# Set the number of threads

# Run the program

In this example, omp_hello is a demo program contained in the vsc-tutorial module that will simply print a message from each thread. the vsc-tutorial module also loads the intel/2020a module as that compiler was used to compile the programs in the module.

When using Intel OpenMP, not setting the variable OMP_NUM_THREADS works fine in most or even all cases as the runtime seems to recognize Slurm and pick up the right number of threads.

In many cases it may be beneficial to also set

export OMP_PROC_BIND=true

before calling the OpenMP program. It will spread the OpenMP threads over the cores and keep them bound to the core, which is beneficial for reuse of data in caches. Except for really short running threads (as is the case in this example) the Linux scheduler will usually rather quickly distribute the threads across the cores and keep them pinned. Yet there are cases where OMP_PROC_BIND=true turns out to be beneficial for performance.

MPI program

Running distributed memory programs usually requires a program starter. In the case of MPI programs, the usual way to start a program is through the mpirun or mpiexec command. The major MPI implementations will recognize Slurm (sometimes with the help of some environment variables) and work with Slurm to start the MPI processes on the correct cores and under the control of the resource manager (so that they are cleaned up properly if things go wrong).

However, with several implementations (and all MPI implementations supported on our cluster), it is also possible to use the Slurm srun command to start the MPI processes. This is the case for programs compiled with Intel MPI as the example below shows. The example assumes there is a MPI program called mpi_hello provided by the vsc-tutorial module. The vsc-tutorial module will also load the intel/2020a module for the libraries used by the compiler and the MPI library.

#! /bin/bash
#SBATCH --job-name=mpihello
#SBATCH --ntasks=56 --cpus-per-task=1 --mem-per-cpu=512m
#SBATCH --time=5:00

# Build the environment
module --force purge
module load calcua/2020a
module load vsc-tutorial

# Run the MPI program
srun mpi_hello

In the above case, 56 MPI ranks will be spawned, corresponding to two nodes on a cluster with 28 cores per node.

Note that if the executable called by srun is not in the path, you do need to specify the path to the executable just as you would have to do on the command line. E.g., if mpi_hello would be in the current directory in the above example, it would be started using

srun ./mpi_hello

and of course you’d have to load a module with MPI support.

Hybrid MPI/OpenMP program

Some programs are hybrids combining a distributed memory technology with a shared memory technology. The idea is that shared memory doesn’t scale beyond a single node (and often not even to the level of a single node), while distributed memory programs that spawn a process per core may also suffer from too much memory and communication overhead. Combining both can sometimes give better performance for a given number of cores.

Especially the combination of MPI and OpenMP is popular. Such programs require a program starter and need the number of threads to be set in one way or another. With many MPI implementations (including the ones we use at the VSC), srun is an ideal program starter and will start the hybrid MPI/OpenMP processes on the right sets of cores.

The example below assumes mpi_omp_hello is a program compiled with the Intel toolchain that uses both MPI and OpenMP. It starts 8 processes with 7 threads each, so it would occupy two nodes on a cluster with 28 cores per node.

#! /bin/bash
#SBATCH --ntasks=8 --cpus-per-task=7 --mem-per-cpu=512m
#SBATCH --time=5:00
#SBATCH --job-name=hybrid-hello-test

module --force purge
module load calcua/supported
module load vsc-tutorial

# Set the number of threads per MPI rank
export OMP_PROC_BIND=true

# Start the application
srun mpi_omp_hello

As with shared memory programs, it turns out that setting OMP_NUM_THREADS is not needed most of the time when the Intel compilers were used for the application as they pick up the correct number of threads from Slurm. We did set OMP_PROC_BIND to true as binding threads to core is as essential as it is for regular shared memory programs.

Job arrays and parameter exploration

Slurm manual page on job array

Slurm also supports job arrays. This is a mechanism to submit and manage a collection of similar jobs simultaneously much more efficiently then when they are submitted as many regular jobs. When submitting a job array, a range of index values is given. The job script is then started for each of the index values and that value is passed to the job through the SLURM_ARRAY_TASK_ID variable.

E.g., assume that there is a program called test_set in the current directory that reads from an input file and writes to an output file, and assume that we want run this for a set of input files named input_1.dat to input_100.dat, writing the output to output_1.dat till output_100.dat. The job script would look like:

#! /bin/bash -l

#SBATCH --ntasks=1 --cpus-per-task=1
#SBATCH --mem-per-cpu=512M
#SBATCH --time 15:00


./test_set ${SLURM_ARRAY_TASK_ID} -input ${INPUT_FILE}  -output ${OUTPUT_FILE}

Assume the filename of the script is job_array.slurm, then it would be submitted using

sbatch --array=1-100 job_array.slurm

Within the VSC, the package atools was developed to ease management of job arrays and to start programs using parameter values stored in a CSV file that can be generated easily using a spreadsheet program. On the UAntwerp clusters, the most recent version of the package is available as the module atools/slurm. For information on how to use atools, we refer to the atools documentation on ReadTheDocs and course material from a course at KU Leuven. Note however that the Worker package which is also mentioned in that course does not work on Slurm and can no longer be used.

Workflow through job dependencies

Consider the following example

  • We run a simulation to compute a first solution.

  • After the simulation, we add two different sized perturbations to the solution and run again from these perturbed states.

Of course, one could try to do all three simulations in a single job script, but that is not a good idea for various reasons.

  • Longer-running jobs may have a lower priority in the scheduler

  • When there is a failure halfway the job, it may take some puzzling to figure out which parts have to run again and to adapt the job script to that.

  • As the simulations from the perturbed state are independent of each other, it is possible to run them in parallel rather then sequentially.

On the other hand, first launching the simulation that computes the first solution, then waiting until that job has finished and only then launching two jobs, one for each perturbation, isn’t a very handy solution either.

Two elements can be combined to do this in a handier way, submitting all jobs simultaneously:

  • As environment variables are passed to the job script, they can be used to influence the behaviour of a job script. In our example, they could be used to specify the size of the perturbation to apply so that both jobs that run from a perturbed state can be submitted using the same job script.

    An alternative is to pass command line arguments to the job script which is possible in Slurm by adding them to the sbatch command line after the job script.

  • Dependency specifications can then be used to ensure that the jobs that run from a perturbed state do not start before the first simulation has successfully completed.

For example, assume that we have two job scripts:

  • job_first.slurm is a job script that computes the first solution.

  • job_depend.slurm is a job script to compute a solution from a perturbed initial state. It uses the environment variable perturbation_size to determine the perturbation to apply.

To make sbatch print simply the job ID after submitting, use the --parsable option. The following lines automate the launch of the three jobs:

first=$(sbatch --parsable --job-name job_leader job_first.slurm)
perturbation_size='0.05' sbatch --job-name job_pert_0_05 --dependency=afterok:$first job_depend.slurm
perturbation_size='0.1'  sbatch --job-name job_pert_0_1  --dependency=afterok:$first job_depend.slurm

Interactive job

Method 1: with srun

Interactively running shared memory programs

Starting a single task interactive job can be done easily by using srun --pty bash on the command line of one of the login nodes. For example:

login$ srun --ntasks=1 --cpus-per-task=10 --time=10:00 --mem-per-cpu=3G --pty bash

(with login$ denoting the command prompt on the login node) or briefly

login$ srun -n1 -c10 -t10 --mem-per-cpu=3G --pty bash

will start a bash shell on a compute node and allocate 10 cores and 30 GB of memory to that session. The maximum wall time of the job is set to 10 minutes.

Specifying the --pty option redirects the standard and error outputs of the first (and, in this case, only) task to the attached terminal. This effectively results in an interactive bash session on the requested compute node.

Interactively running MPI programs

Interactively running MPI programs or hybrid MPI/OpenMP programs is very similar to the way you run shared memory programs interactively, but just as in batch scripts you now need to request one task per MPI rank and use a process starter to start the program, which usually amounts to running srun in an srun-session. E.g.,

login$ srun -n 64 -c 1 -t 1:00:00 --pty bash

will create an allocation for 64 single-core tasks and start a bash shell in the first task that takes its input from the keyboard and sends its output to the terminal. You can then run a MPI program in the same way as you would in a batch script (with r0c00cn0$ denoting the command prompt of the compute node):

r0c00cn0$ module --force purge
r0c00cn0$ module load calcua/2020a vsc-tutorial
r0c00cn0$ srun mpi_hello
r0c00cn0$ exit

The exit command at the end ends the shell and hence the interactive job.

Running X11 programs

You can also use srun to start an interactive session with X11 support. However, before starting a session you should ensure that you can start X11 programs from the session from you will be starting sriun. So either

  1. ensure you are running an X11 server on the machine from which you log on to the cluster and then log on using ssh -X to forward X11 traffic from the cluster to your local machine,

  2. or use a VNC session on the cluster. VNC without 3D acceleration is supported on all login nodes of the UAntwerp infrastructure and 3D acceleration is supported on the visualisation node. Follow the instructions on the Remote visualisation@UAntwerp page to start a VNC session.

X11 programs rarely use distributed memory parallelism, so in most case you will be requesting just a single task. To add support for X11, use the --x11 option before --pty:

login$ srun -n 1 -c 64 -t 1:00:00 --x11 --pty bash
r0c00cn0$ xclock
r0c00cn0$ exit

would allocate 64 (virtual) cores, and the second line starts a simple X11 program, x11, which is only good to test if X11 programs work but should not be used for other purposes than this on the clusters.

Method 2: With salloc

Non-X11 programs

You can use salloc to create a resource allocation. salloc will wait until the resources are available and then return a shell prompt. Note however that that shell is running on the node where you ran salloc (likely a login node). Contrary to the method just before this one based on srun, the shell is not running on the allocated resources. You can however run commands on the allocated resources via srun.

Note that in particular on clusters with multiple CPU architectures, you need to understand Linux environments and the way they interact with Slurm very well as you are now executing commands in two potentially incompatible sections of the cluster that require different settings in the environment. So if you execute a command in the wrong environment it may run inefficiently, or it may simply fail.

There is no problem with Vaughan though as on that cluster all CPUs are of the same generation.

E.g., to run a shared memory program on 16 (virtual) cores, the following commands can be used:

login$ salloc --ntasks=1 --cpus-per-task=16 --time=1:00:00 --mem-per-cpu=3g

executed on a login node will return a shell on the login node. To then execute commands. it is still better to rebuild the environment just as for a batch script as some modules will use different settings when they load in a Slurm environment. So let’s use this shell to start the demo program omp_hello on the allocated resources:

login$ module --force purge
login$ module load calcua/2020a vsc-tutorial
login$ srun omp_hello
login$ exit

It is essential in this example that omp_hello is started through srun as otherwise it would be running on the login node rather than on the allocated resources. Also do not forget to leave the shell when you have finished your interactive work!

For an MPI or hybrid MPI/OpenMP program you would proceed in exactly the same way, except that the resource request is different to allocate all MPI tasks. E.g., to run the demo program mpi_omp_hello in an interactive shell and using 16 MPI processes and 8 threads per MPI rank, you’d allocate the resources through

login$ salloc --ntasks=16 --cpus-per-task=8 --time=1:00:00 --mem-per-cpu=3g

and then run the program with

login$ module --force purge
login$ module load calcua/2020a vsc-tutorial
login$ srun mpi_omp_hello
login$ exit

Note that since we are using all allocated resources, we don’t need to specify the number of tasks or virtual CPUs to srun. It will take care of of properly distributing the job according to the options specified when calling salloc.

Running a shared memory X11 program with salloc

As you can log on to a compute node while you have resources allocated on that node, you can also use salloc to create an allocation and then use ssh to log on to the node that has been allocated. This only makes sense if you allocate one node or less, as when you log on via ssh, you leave the Slurm environment and hence cannot rely on Slurm anymore to start tasks. Moreover, if you have multiple jobs running on the node, you may inadvertently influence those jobs as any work that you’re doing in that ssh session is not fully under the control of Slurm anymore.

As when using srun, the first step is to ensure that X11 access from the login node to your local screen is properly set up.

Next, starting a session that uses a full node on Vaughan can be done as

login$ salloc -n 1 -c 64 -t 1:00:00
r0c00cn0$ xclock
r0c00cn0$ exit
login$ exit

What this does is:

  1. The first command, executed on the login node, creates an allocation for 64 cores. It returns with a shell prompt on the login node as soon as the allocation is ready.

  2. Next we log on to the allocated compute node using ssh. The -X option is used to forwarded X11 traffic. As we allocate only a single node, $SLURM_JOB_NODELIST is just a single node and can be used as argument to ssh.

  3. It is important to note that now we are in our home directory on the compute node as ssh starts with a clean login shell. We can now execute X11 commands or anything we want to do and is supported on a compute node.

  4. The first exit command leaves the compute node and returns to the login shell, but still within the salloc command.

  5. Hence we need a second exit command to leave the shell created by salloc and free the resources for other users.

So do not forget that you need to exit two shells to free the resources!