Scheduler Fundamentals


Teaching: 30 min
Exercises: 20 min
  • What is a scheduler and why does a cluster need one?

  • How do I launch a program to run on a compute 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 script on the login node, and through the scheduler.

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

  • Inspect the output and error files of your jobs.

  • Find the right place to put large datasets on the cluster.

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.


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.

Interactive vs Batch

So far, whenever we have entered a command into our terminals, we have received the response immediately in the same terminal, this is said to be an interactive session.

This is all well for doing small tasks, but what if we want to do several things one after another without without waiting in-between? Or what if we want to repeat a series of command again later?

This is where batch processing becomes useful, this is where instead of entering commands directly to the terminal we write them down in a text file or script. Then, the script can be executed by calling it with bash.

Lets try this now, create and open a new file in your current directory called (If you prefer another text editor than nano, feel free to use that), we will put to use some things we have learnt so far.

[yourUsername@mahuika ~]$ nano
#!/bin/bash -e

module load R/4.3.1-gimkl-2022a
Rscript  array_sum.r 
echo "Done!"


shebang or shabang, also referred to as hashbang is the character sequence consisting of the number sign (aka: hash) and exclamation mark (aka: bang): #! at the beginning of a script. It is used to describe the interpreter that will be used to run the script. In this case we will be using the Bash Shell, which can be found at the path /bin/bash. The job scheduler will give you an error if your script does not start with a shebang.

We can now run this script using

[yourUsername@mahuika ~]$ bash
Loading required package: foreach
Loading required package: iterators
Loading required package: parallel
[1] "Using 1 cpus to sum [ 2.000000e+04 x 2.000000e+04 ] matrix."
[1] "0% done..."
[1] "99% done..."
[1] "100% done..."
[1] "Sum is '10403.632886'."

You will get the output printed to your terminal as if you had just run those commands one after another.

Cancelling Commands

You can kill a currently running task by pressing the keys ctrl + c. If you just want your terminal back, but want the task to continue running you can ‘background’ it by pressing ctrl + v. Note, a backgrounded task is still attached to your terminal session, and will be killed when you close the terminal (if you need to keep running a task after you log out, have a look at tmux).

Scheduled Batch Job

Up until now the scheduler has not been involved, our scripts were run directly on the login node (or Jupyter node).

First lets rename our batch script script to clarify that we intend to run it though the scheduler.


File Extensions

A files extension in this case does not in any way affect how a script is read, it is just another part of the name used to remind users what type of file it is. Some common conventions:
.sh: Shell Script.
.sl: Slurm Script, a script that includes a slurm header and is intended to be submitted to the cluster.
.out: Commonly used to indicate the file contains the stdout of some process.
.err: Same as .out but for stderr.

In order for the job scheduler to do it’s job we need to provide a bit more information about our script. This is done by specifying slurm parameters in our batch script. Each of these parameters must be preceded by the special token #SBATCH and placed after the shebang, but before the content of the rest of your script.


These parameters tell SLURM things around how the script should be run, like memory, cores and time required.

All the parameters available can be found by checking man sbatch or on the online slurm documentation.

--job-name #SBATCH --job-name=MyJob The name that will appear when using squeue or sacct
--account #SBATCH --account=nesi99991 The account your core hours will be 'charged' to.
--time #SBATCH --time=DD-HH:MM:SS Job max walltime
--mem #SBATCH --mem=1500M Memory required per node.
--output #SBATCH --output=%j_output.out Path and name of standard output file.
--ntasks #SBATCH --ntasks=2 Will start 2 MPI tasks.
--cpus-per-task #SBATCH --cpus-per-task=10

Will request 10 logical CPUs per task.

See Hyperthreading.


Comments in UNIX shell scripts (denoted by #) are ignored by the bash interpreter. Why is it that we start our slurm parameters with # if it is going to be ignored?


Commented lines are ignored by the bash interpreter, but they are not ignored by slurm. The #SBATCH parameters are read by slurm when we submit the job. When the job starts, the bash interpreter will ignore all lines starting with #.

This is similar to the shebang mentioned earlier, when you run your script, the system looks at the #!, then uses the program at the subsequent path to interpret the script, in our case /bin/bash (the program ‘bash’ found in the ‘bin’ directory).

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.

Now, rather than running our script with bash we submit it to the scheduler using the command sbatch (slurm batch).

[yourUsername@mahuika ~]$ sbatch
Submitted batch job 23137702

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.

Checking on Running/Pending Jobs

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 (slurm queue). We will need to filter to see only our jobs, by including either the flag --user <username> or --me.

[yourUsername@mahuika ~]$ squeue --me
231964  yourUsername nesi99991 1    512M     large   N/A        1:00     PENDING  (Priority)

We can see many details about our job, most importantly is it’s STATE, the most common states you might see are..

Cancelling Jobs

Sometimes we’ll make a mistake and need to cancel a job. This can be done with the scancel command.

In order to cancel the job, we will first need its ‘JobId’, this can be found in the output of ‘squeue –me’.

[yourUsername@mahuika ~]$ scancel 231964

A clean return of your command prompt indicates that the request to cancel the job was successful.

Now checking squeue again, the job should be gone.

[yourUsername@mahuika ~]$ squeue --me

(If it isn’t wait a few seconds and try again).

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@mahuika ~]$ sbatch
[yourUsername@mahuika ~]$ sbatch
[yourUsername@mahuika ~]$ sbatch

Then, cancel them all:

[yourUsername@mahuika ~]$ scancel --user yourUsername

Checking Finished Jobs

There is another command sacct (slurm account) that includes jobs that have finished. By default sacct only includes jobs submitted by you, so no need to include additional commands at this point.

[yourUsername@mahuika ~]$ sacct
JobID           JobName          Alloc     Elapsed     TotalCPU  ReqMem   MaxRSS State      
--------------- ---------------- ----- ----------- ------------ ------- -------- ---------- 
31060451       2    00:00:48    00:33.548      1G          CANCELLED  
31060451.batch  batch                2    00:00:48    00:33.547          102048K CANCELLED  
31060451.extern extern               2    00:00:48     00:00:00                0 CANCELLED  

Note that despite the fact that we have only run one job, there are three lines shown, this because each job step is also shown. This can be suppressed using the flag -X.

Where’s the Output?

On the login node, when we ran the bash script, the output was printed to the terminal. Slurm batch job output is typically redirected to a file, by default this will be a file named slurm-<job-id>.out in the directory where the job was submitted, this can be changed with the slurm parameter --output.


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@mahuika ~]$ man sbatch

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@mahuika ~]$ nano
[yourUsername@mahuika ~]$ cat
#!/bin/bash -e
#SBATCH --time 00:00:30

echo -n "This script is running on "

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

Key Points

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

  • A job is just a shell script.

  • Request slightly more resources than you will need.