HPC novice

Batch systems and schedulers 101


Teaching: 45 min
Exercises: 0 min
  • Why is a scheduler necessary?

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

  • How do I capture the output of a program that is run on a remote host?

  • 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.


When large corporate or academic institutions acquire super computers or compute clusters, the competition among users to obtain time to run programs is typically fierce. On the other hand, expectations by users are high, that their code will run very fast and provide them with answers to their scientific questions at a high turn-around rate. As supercomputers are multi-purpose machines, they are required not only to provide good or even excellent performance, but also that they distribute this performance among all users in a fair manner.

On a laptop or workstation any request for CPU time is immediately handled by the operating system. Just click any link on this page and the OS will immediately hand your request through to the CPU, which in turn will execute the routines and functions in your browser which are eventually required to ask the server what contents lie behind the link that you just clicked, so that the website of that link is displayed in your browser.

If a super computer would operate like this, very soon, nobody would use it anymore. The reason is that typically your laptop or workstation is a computational resource that is used mostly by one user only - namely you. This user’s requests will be handled exclusively.

A super computer is a shared resource. Many users can log into it at the same time and work on your code, perform computations of a wide variety and perform operations on files and folders at the same time as well. In order to prevent clashes of tasks that require time on the machine exclusively, every action on the cluster needs to be described by a user. An application called the job scheduler then uses this description to decide when and where to best place the action for execution.

Lola’s lab has just bought a 4-node cluster for their sole use. Each individual computer in the cluster (also called a node) has 12 CPU cores, some memory and a connection to a network that all nodes are members of and can hence communicate by. This is by far much more hardware than the group has ever managed before. The setup is different from the cluster at the IT department that Rob started to introduce her to.

Schematics of a 4-node cluster

She and her colleague start to run calculations on the machine. Lola wants to analyze some files that are scattered across the cluster.

Lola's jobs on the 4-node cluster

Lola’s colleague is tasked to check on the cluster every now and then. She wrote a program, that collects some telemetry data from each node every at fixed time intervals. Today, some more detailed statistics have to be collected from every node. As this task can be performed for every node independent of it’s neighboring node, Lola’s room mate performs it in parallel.

Lola's (blue) and her room mates (red) compute jobs on the 4-node cluster

Both discover independently that the compute tasks on node 0 take longer than usual. Lola’s jobs on node 0 require all 12 CPU cores to run. If a new job comes in and wants to perform a task, the node has to decide which task has the higher priority. For sure, this decision is not made based on the sender of the task but on other parameters. Operating systems (just as job schedulers) try to distribute the compute power of a machine in a fair share fashion as well. But that means, that these two tasks will take longer than expected as they block each other or they steal each others resources.

For this very reason, a job scheduler is put in place on a compute cluster that manages tasks on it. It accepts task (or better ‘job’) descriptions. Based on the current usage of the nodes, it can then decide when a task is launched (or dispatched or spooled in HPC speak) on a node. The scheduler also helps treating the output of a job to forward it to the submitter’s terminal or write it to a file.

The cluster in question here, is running a scheduling software called slurm (a scheduler is also referred to as batch system). After this incident, Lola approaches her lab mate and they both decide to give the batch system a try.

A first exercise would be to submit a job that does nothing else but print “Hello World!”.

$ srun echo Hello World
Hello World

That worked out pretty well. The problem is, it’s not very helpful and doesn’t help Lola or anyone to do her job. But Lola wonders if the job really was executed on another node. She thinks of a little experiment to explore the scheduler a bit.

$ salloc  
$ srun hostname

If she repeats this command, over and over again, the output changes. So these commands must be running on another node.

Individual commands are fine, but Lola knows from experience that very often her work requires her to do more complex tasks, i.e. to execute a couple of commands after one another. To simulate this, she writes a small script that can be run on the node that runs her job.

$ cat multiple_commands.sh

$ sbatch < multiple_commands.sh
$ cat slurm-6319373.out
Tue Mar  7 11:04:11 CET 2018

Editing a file on a remote machine

The question is, how to do that?

You have several options:

  1. run a editor inside the ssh session that you opened to work on the cluster. Note that you can only run applications which the HPC administrator has installed (mostly vi/vim, emacs, nano or pico are programs commonly installed on HPC machines).
  2. connect to the cluster with ssh using the ssh -X switch, if done so, you can open editors like emacs, nedit, gedit, … that are capable of spinning up a GUI (careful though, the GUI contents need to be transmitted through the network from the cluster to your workstation or laptop and vice verse, so in case you have a poor network connection, this approach can be visually painful)
  3. use remote editing capabilities of your preferred editor or IDE (e.g. emacs and vim both have a built-in packages for this, check your preferred IDE manual for details)
  4. have a folder of your remote host mounted on your laptop (the details depend on the remote cluster and you should get in touch with the admin to find out what technologies are available), edit the files inside this folder (most of the time they are updated to the cluster in real-time) and launch the applications from your ssh session

OK, but Lola wonders where the output of the job goes. Is there a way to reliably store the output of the command permanently? Reading the documentation of the submit command, she finds out that the output of her command can be stored into a file of Lola’s liking.

$ cat multiple_commands.sh

$ sbatch -o multiple_commands.log < multiple_commands.sh

Once the job is done, the file multiple_commands.log is created in the current directory and yields the following output:

$ cat multiple_commands.log
Tue Mar  7 11:14:11 CET 2017

Errors are important as well

The *nix operating systems have some special quirks to it. One of it is, that there is not only one way to print something to the terminal. There is the so called standard output and standard error output. Text that is sent to one of them is not seen by the other. Typically, error messages are sent to the standard error (the word output is often omitted) and status messages are sent to standard output. When executing commands on the command line, the difference between the two ways is not noticeable interactively.

The scheduler allows Lola to split the two and write them to individual files. For example like this:

$ cat multiple_commands.sh

$ sbatch -o multiple_commands.log -e multiple_commands.err  < multiple_commands.sh

Fill me in

Your colleague left you with a small script ~/check_node.sh that needs to be run BEFORE any other application to check if a node has enough free memory using the free command. This is essential to reproduce her paper. You are experimenting with it to find out, if it works on your cluster too. Fill in the blank spots.

$ cat ________


$ _____ -o multiple_commands.log ________

How long has this node been running ?

Write a batch script that prints the hostname and the time the node has been running so far (using the uptime command). Advice the scheduler to store the output in a log file. Submit this script multiple times and see, if you find a node that has been running the longest or the shortest. Compare with your neighbor!

Key Points