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

Using shared resources responsibly


Teaching: 15 min
Exercises: 5 min
  • How can I be a responsible user?

  • How can I protect my data?

  • How can I best get large amounts of data off an HPC system?

  • Learn how to be a considerate shared system citizen.

  • Understand how to protect your critical data.

  • Appreciate the challenges with transferring large amounts of data off HPC systems.

  • Understand how to convert many files to a single archive file using tar.

One of the major differences between using remote HPC resources and your own system (e.g. your laptop) is that they are a shared resource. How many users the resource is shared between at any one time varies from system to system but it is unlikely you will ever be the only user logged into or using such a system.

We have already mentioned one of the consequences of this shared nature of the resources: the scheduling system where you submit your jobs, but there are other things you need to consider in order to be a considerate HPC citizen, to protect your critical data and to transfer data

Be kind to the login nodes

The login node is often very busy managing lots of users logged in, creating and editing files and compiling software! It doesn’t have any extra space to run computational work.

Don’t run jobs on the login node (though quick tests are generally fine). A “quick test” is generally anything that uses less than 5 minutes of time. If you use too much resource then other users on the login node will start to be affected - their login sessions will start to run slowly and may even freeze or hang.

Login nodes are a shared resource

Remember, the login node is shared with all other users and your actions could cause issues for other people. Think carefully about the potential implications of issuing commands that may use large amounts of resource.

You can always use the commands top and ps ux to list the processes you are running on a login node and the amount of CPU and memory they are using. The kill command can be used along with the PID to terminate any processes that are using large amounts of resource.

Login Node Etiquette

Which of these commands would probably be okay to run on the login node?

  1. python
  2. make
  4. molecular_dynamics_2
  5. tar -xzf R-3.3.0.tar.gz


Building software, creating directories, and unpacking software are common and acceptable tasks for the login node: options #2 (make), #3 (mkdir), and #5 (tar) are probably OK. Note that script names do not always reflect their contents: before launching #3, please less and make sure it’s not a Trojan horse.

Running resource-intensive applications is frowned upon. Unless you have cleared it with the system administrators, do not run #1 (python) or #4 (custom MD code).

If you experience performance issues with a login node you should report it to the system staff (usually via the helpdesk) for them to investigate. You can use the top command to see which users are using which resources.

Test before scaling

Remember that you are generally charged for usage on shared systems. A simple mistake in a job script can end up costing a large amount of resource budget. Imagine a job script with a mistake that makes it sit doing nothing for 24 hours on 1000 cores or one where you have requested 2000 cores by mistake and only use 100 of them! This problem can be compounded when people write scripts that automate job submission (for example, when running the same calculation or analysis over lots of different input). When this happens it hurts both you (as you waste lots of charged resource) and other users (who are blocked from accessing the idle compute nodes).

On very busy resources you may wait many days in a queue for your job to fail within 10 seconds of starting due to a trivial typo in the job script. This is extremely frustrating! Most systems provide dedicated resources for testing that have short wait times to help you avoid this issue.

Test job submission scripts that use large amounts of resources

Before submitting a large run of jobs, submit one as a test first to make sure everything works as expected.

Before submitting a very large or very long job submit a short truncated test to ensure that the job starts as expected.

Have a backup plan

Although many HPC systems keep backups, it does not always cover all the file systems available and may only be for disaster recovery purposes (i.e. for restoring the whole file system if lost rather than an individual file or directory you have deleted by mistake). Your data on the system is primarily your responsibility and you should ensure you have secure copies of data that are critical to your work.

Version control systems (such as Git) often have free, cloud-based offerings (e.g. Github, Gitlab) that are generally used for storing source code. Even if you are not writing your own programs, these can be very useful for storing job scripts, analysis scripts and small input files.

For larger amounts of data, you should make sure you have a robust system in place for taking copies of critical data off the HPC system wherever possible to backed-up storage. Tools such as rsync can be very useful for this.

Your access to the shared HPC system will generally be time-limited so you should ensure you have a plan for transferring your data off the system before your access finishes. The time required to transfer large amounts of data should not be underestimated and you should ensure you have planned for this early enough (ideally, before you even start using the system for your research).

In all these cases, the helpdesk of the system you are using should be able to provide useful guidance on your options for data transfer for the volumes of data you will be using.

Your data is your responsibility

Make sure you understand what the backup policy is on the file systems on the system you are using and what implications this has for your work if you lose your data on the system. Plan your backups of critical data and how you will transfer data off the system throughout the project.

Transferring data

As mentioned above, many users run into the challenge of transferring large amounts of data off HPC systems at some point (this is more often in transferring data off than onto systems but the advice below applies in either case). Data transfer speed may be limited by many different factors so the best data transfer mechanism to use depends on the type of data being transferred and where the data is going. Some of the key issues to be aware of are:

As mentioned above, if you have related data that consists of a large number of small files it is strongly recommended to pack the files into a larger archive file for long term storage and transfer. A single large file makes more efficient use of the file system and is easier to move, copy and transfer because significantly fewer meta-data operations are required. Archive files can be created using tools like tar and zip. We have already met tar when we talked about data transfer earlier.

Consider the best way to transfer data

If you are transferring large amounts of data you will need to think about what may affect your transfer performance. It is always useful to run some tests that you can use to extrapolate how long it will take to transfer your data.

Say you have a “data” folder containing 10,000 or so files, a healthy mix of small and large ASCII and binary data. Which of the following would be the best way to transfer them to Myriad?

  1. [user@laptop ~]$ scp -r data
  2. [user@laptop ~]$ rsync -ra data
  3. [user@laptop ~]$ rsync -raz data
  4. [user@laptop ~]$ tar -cvf data.tar data
    [user@laptop ~]$ rsync -raz data.tar
  5. [user@laptop ~]$ tar -cvzf data.tar.gz data
    [user@laptop ~]$ rsync -ra data.tar.gz


  1. scp will recursively copy the directory. This works, but without compression.
  2. rsync -ra works like scp -r, but preserves file information like creation times. This is marginally better.
  3. rsync -raz adds compression, which will save some bandwidth. If you have a strong CPU at both ends of the line, and you’re on a slow network, this is a good choice.
  4. This command first uses tar to merge everything into a single file, then rsync -z to transfer it with compression. With this large number of files, latency per-file can hamper your transfer, so this is a good idea.
  5. This command uses tar -z to compress the archive, then rsync to transfer it. This may perform similarly to #4, but in most cases (for large datasets), it’s the best combination of high throughput and low latency (making the most of your time and network connection).

Key Points

  • Be careful how you use the login node.

  • Your data on the system is your responsibility.

  • Plan and test large data transfers.

  • It is often best to convert many files to a single archive file before transferring.

  • Again, don’t run stuff on the login node.

  • Don’t be a bad person and run stuff on the login node.