This lesson is in the early stages of development (Alpha version)

Working with the scheduler


Teaching: 45 min
Exercises: 30 min
  • What is a scheduler and why are they used?

  • How do I launch a program to run on any one node in the cluster?

  • How do I capture the output of a program that is run on a node in the cluster?

  • Run a simple Hello World style program on the cluster.

  • Submit a simple Hello World style script to the cluster.

  • Use the batch system command line tools to monitor the execution of your job.

  • Inspect the output and error files of your jobs.

Job scheduler

An HPC system might have thousands of nodes and thousands of users. How do we decide who gets what and when? How do we ensure that a task is run with the resources it needs? This job is handled by a special piece of software called the scheduler. On an HPC system, the scheduler manages which jobs run where and when.

The following illustration compares these tasks of a job scheduler to a waiter in a restaurant. If you can relate to an instance where you had to wait for a while in a queue to get in to a popular restaurant, then you may now understand why sometimes your job do not start instantly as in your laptop.


Job scheduling roleplay (optional)

Your instructor will divide you into groups taking on different roles in the cluster (users, compute nodes and the scheduler). Follow their instructions as they lead you through this exercise. You will be emulating how a job scheduling system works on the cluster.


To do this exercise, you will need about 50-100 pieces of paper or sticky notes.

  1. Divide the room into groups, with specific roles.
    • Pick three or four people to be the “scheduler.”
    • Select one-third of the room be “users”, given several slips of paper (or post-it notes) and pens.
    • Have the remaining two thirds of the room be “compute nodes.”
    • Have the “users” go to the front of the room (or the back, wherever there’s space for them to stand) and the “schedulers” stand between the users and “compute nodes” (who should remain at their seats).
  2. Divide the pieces of paper / sticky notes among the “users” and have them fill out all the pages with simple math problems and their name. Tell everyone that these are the jobs that need to be done and correspond to their computing research problems.

  3. Point out that we now have jobs and we have “compute nodes” (the people still sitting down) that can solve these problems. How are the jobs going to get to the nodes? The answer is the scheduling program that will take the jobs from the users and deliver them to open compute nodes.

  4. Have all the “compute nodes” raise their hands. Have the users “submit” their jobs by handing them to the schedulers. Schedulers should then deliver them to “open” (hands-raised) compute nodes and collect finished problems and return them to the appropriate user.

  5. Wait until most of the problems are done and then re-seat everyone.


A “node” might be unable to solve the assigned problem for a variety of reasons.

  • Ran out of time.
  • Ran out of memory.
  • Ran out of storage space, or could not load an input file or dataset.
  • Doesn’t know where to start: nobody “taught” it, i.e., the program can’t be loaded.
  • Gets stuck on a hard part: the program has the wrong algorithm, or was never told to load the library containing the right algorithm.
  • Was busy thinking about something else, and didn’t get to the problem yet.

The scheduler used in this lesson is SLURM. Although SLURM is not used everywhere, running jobs is quite similar regardless of what software is being used. The exact syntax might change, but the concepts remain the same.

Running a batch job

The most basic use of the scheduler is to run a command non-interactively. Any command (or series of commands) that you want to run on the cluster is called a job, and the process of using a scheduler to run the job is called batch job submission.

In this case, the job we want to run is just a shell script. Let’s create a demo shell script to run as a test. The landing pad will have a number of terminal-based text editors installed. Use whichever you prefer. Unsure? nano is a pretty good, basic choice.

[yourUsername@gra-login1 ~]$ nano
[yourUsername@gra-login1 ~]$ chmod +x
[yourUsername@gra-login1 ~]$ cat
#!/usr/bin/env bash

echo -n "This script is running on "

Creating our test job

Run the script. Does it execute on the cluster or just our login node?


[yourUsername@gra-login1 ~]$ ./
This script is running on gra-login1

This job runs on the login node.

If you completed the previous challenge successfully, you probably realise that there is a distinction between running the job through the scheduler and just “running it”. To submit this job to the scheduler, we use the sbatch command.

[yourUsername@gra-login1 ~]$ sbatch
Submitted batch job 36855

And that’s all we need to do to submit a job. Our work is done — now the scheduler takes over and tries to run the job for us. While the job is waiting to run, it goes into a list of jobs called the queue. To check on our job’s status, we check the queue using the command squeue -u yourUsername.

[yourUsername@gra-login1 ~]$ squeue -u yourUsername
JOBID USER         ACCOUNT     NAME           ST REASON START_TIME         T...
36856 yourUsername yourAccount R  None   2017-07-01T16:47:02 ...

We can see all the details of our job, most importantly that it is in the R or RUNNING state. Sometimes our jobs might need to wait in a queue (PENDING) or have an error (E).

The best way to check our job’s status is with squeue. Of course, running squeue repeatedly to check on things can be a little tiresome. To see a real-time view of our jobs, we can use the watch command. watch reruns a given command at 2-second intervals. This is too frequent, and will likely upset your system administrator. You can change the interval to a more reasonable value, for example 15 seconds, with the -n 15 parameter. Let’s try using it to monitor another job.

[yourUsername@gra-login1 ~]$ sbatch
[yourUsername@gra-login1 ~]$ watch -n 15 squeue -u yourUsername

You should see an auto-updating display of your job’s status. When it finishes, it will disappear from the queue. Press Ctrl-c when you want to stop the watch command.

Where’s the output?

On the login node, this script printed output to the terminal — but when we exit watch, there’s nothing. Where’d it go?

Cluster job output is typically redirected to a file in the directory you launched it from. Use ls to find and read the file.

Customising a job

The job we just ran used all of the scheduler’s default options. In a real-world scenario, that’s probably not what we want. The default options represent a reasonable minimum. Chances are, we will need more cores, more memory, more time, among other special considerations. To get access to these resources we must customize our job script.

Comments in UNIX shell scripts (denoted by #) are typically ignored, but there are exceptions. For instance the special #! comment at the beginning of scripts specifies what program should be used to run it (you’ll typically see #!/usr/bin/env bash). Schedulers like SLURM also have a special comment used to denote special scheduler-specific options. Though these comments differ from scheduler to scheduler, SLURM’s special comment is #SBATCH. Anything following the #SBATCH comment is interpreted as an instruction to the scheduler.

Let’s illustrate this by example. By default, a job’s name is the name of the script, but the -J option can be used to change the name of a job. Add an option to the script:

[yourUsername@gra-login1 ~]$ cat
#!/usr/bin/env bash
#SBATCH -J new_name

echo -n "This script is running on "
echo "This script has finished successfully."

Submit the job and monitor its status:

[yourUsername@gra-login1 ~]$ sbatch
[yourUsername@gra-login1 ~]$ squeue -u yourUsername
38191 yourUsername yourAccount new_name PD Priority N/A        0:00 1:00:00 ...

Fantastic, we’ve successfully changed the name of our job!

Setting up email notifications

Jobs on an HPC system might run for days or even weeks. We probably have better things to do than constantly check on the status of our job with squeue. Looking at the manual page for sbatch, can you set up our test job to send you an email when it finishes?


You can use the manual pages for SLURM utilities to find more about their capabilities. On the command line, these are accessed through the man utility: run man <program-name>. You can find the same information online by searching > “man ".

[yourUsername@gra-login1 ~]$ man sbatch

Resource requests

But what about more important changes, such as the number of cores and memory for our jobs? One thing that is absolutely critical when working on an HPC system is specifying the resources required to run a job. This allows the scheduler to find the right time and place to schedule our job. If you do not specify requirements (such as the amount of time you need), you will likely be stuck with your site’s default resources, which is probably not what you want.

The following are several key resource requests:

Note that just requesting these resources does not make your job run faster, nor does it necessarily mean that you will consume all of these resources. It only means that these are made available to you. Your job may end up using less memory, or less time, or fewer tasks or nodes, than you have requested, and it will still run.

It’s best if your requests accurately reflect your job’s requirements. We’ll talk more about how to make sure that you’re using resources effectively in a later episode of this lesson.

Submitting resource requests

Modify our hostname script so that it runs for a minute, then submit a job for it on the cluster.


[yourUsername@gra-login1 ~]$ cat
#!/usr/bin/env bash
#SBATCH -t 00:01:15

echo -n "This script is running on "
sleep 60 # time in seconds
echo "This script has finished successfully."
[yourUsername@gra-login1 ~]$ sbatch

Why are the SLURM runtime and sleep time not identical?

Job environment variables

When SLURM runs a job, it sets a number of environment variables for the job. One of these will let us check what directory our job script was submitted from. The SLURM_SUBMIT_DIR variable is set to the directory from which our job was submitted. Using the SLURM_SUBMIT_DIR variable, modify your job so that it prints out the location from which the job was submitted.


[yourUsername@gra-login1 ~]$ nano
[yourUsername@gra-login1 ~]$ cat
#!/usr/bin/env bash
#SBATCH -t 00:00:30

echo -n "This script is running on "

echo "This job was launched in the following directory:"

Resource requests are typically binding. If you exceed them, your job will be killed. Let’s use walltime as an example. We will request 30 seconds of walltime, and attempt to run a job for two minutes.

[yourUsername@gra-login1 ~]$ cat
#!/usr/bin/env bash
#SBATCH -J long_job
#SBATCH -t 00:00:30

echo "This script is running on ... "
sleep 120 # time in seconds
echo "This script has finished successfully."

Submit the job and wait for it to finish. Once it is has finished, check the log file.

[yourUsername@gra-login1 ~]$ sbatch
[yourUsername@gra-login1 ~]$ watch -n 15 squeue -u yourUsername
[yourUsername@gra-login1 ~]$ cat slurm-38193.out
This job is running on:
slurmstepd: error: *** JOB 38193 ON gra533 CANCELLED AT 2017-07-02T16:35:48

Our job was killed for exceeding the amount of resources it requested. Although this appears harsh, this is actually a feature. Strict adherence to resource requests allows the scheduler to find the best possible place for your jobs. Even more importantly, it ensures that another user cannot use more resources than they’ve been given. If another user messes up and accidentally attempts to use all of the cores or memory on a node, SLURM will either restrain their job to the requested resources or kill the job outright. Other jobs on the node will be unaffected. This means that one user cannot mess up the experience of others, the only jobs affected by a mistake in scheduling will be their own.

Cancelling a job

Sometimes we’ll make a mistake and need to cancel a job. This can be done with the scancel command. Let’s submit a job and then cancel it using its job number (remember to change the walltime so that it runs long enough for you to cancel it before it is killed!).

[yourUsername@gra-login1 ~]$ sbatch
[yourUsername@gra-login1 ~]$ squeue -u yourUsername
Submitted batch job 38759

38759 yourUsername yourAccount PD Priority 0:00 1:00      1  ...

Now cancel the job with its job number (printed in your terminal). A clean return of your command prompt indicates that the request to cancel the job was successful.

[yourUsername@gra-login1 ~]$ scancel 38759
# It might take a minute for the job to disappear from the queue...
[yourUsername@gra-login1 ~]$ squeue -u yourUsername

Cancelling multiple jobs

We can also cancel all of our jobs at once using the -u option. This will delete all jobs for a specific user (in this case, yourself). Note that you can only delete your own jobs.

Try submitting multiple jobs and then cancelling them all.


First, submit a trio of jobs:

[yourUsername@gra-login1 ~]$ sbatch
[yourUsername@gra-login1 ~]$ sbatch
[yourUsername@gra-login1 ~]$ sbatch

Then, cancel them all:

[yourUsername@gra-login1 ~]$ scancel -u yourUsername

Other types of jobs

Up to this point, we’ve focused on running jobs in batch mode. SLURM also provides the ability to start an interactive session.

There are very frequently tasks that need to be done interactively. Creating an entire job script might be overkill, but the amount of resources required is too much for a login node to handle. A good example of this might be building a genome index for alignment with a tool like HISAT2. Fortunately, we can run these types of tasks as a one-off with srun.

srun runs a single command on the cluster and then exits. Let’s demonstrate this by running the hostname command with srun. (We can cancel an srun job with Ctrl-c.)

[yourUsername@gra-login1 ~]$ srun hostname

srun accepts all of the same options as sbatch. However, instead of specifying these in a script, these options are specified on the command-line when starting a job. To submit a job that uses 2 CPUs for instance, we could use the following command:

[yourUsername@gra-login1 ~]$ srun -n 2 echo "This job will use 2 CPUs."
This job will use 2 CPUs.
This job will use 2 CPUs.

Typically, the resulting shell environment will be the same as that for sbatch.

Interactive jobs

Sometimes, you will need a lot of resource for interactive use. Perhaps it’s our first time running an analysis or we are attempting to debug something that went wrong with a previous job. Fortunately, SLURM makes it easy to start an interactive job with srun:

[yourUsername@gra-login1 ~]$ srun --pty bash

You should be presented with a bash prompt. Note that the prompt will likely change to reflect your new location, in this case the compute node we are logged on. You can also verify this with hostname.

Creating remote graphics

To see graphical output inside your jobs, you need to use X11 forwarding. To connect with this feature enabled, use the -Y option when you login with the ssh command, e.g., ssh -Y

To demonstrate what happens when you create a graphics window on the remote node, use the xeyes command. A relatively adorable pair of eyes should pop up (press Ctrl-C to stop). If you are using a Mac, you must have installed XQuartz (and restarted your computer) for this to work.

If your cluster has the slurm-spank-x11 plugin installed, you can ensure X11 forwarding within interactive jobs by using the --x11 option for srun with the command srun --x11 --pty bash.

When you are done with the interactive job, type exit to quit your session.

Key Points

  • The scheduler handles how compute resources are shared between users.

  • Everything you do should be run through the scheduler.

  • A job is just a shell script.

  • If in doubt, request more resources than you will need.