Even if you don't do software development yourself (and software development includes, e.g., developing R- or Matlab routines), working on a supercomputer differs from using a PC, so some training is useful for everybody.

Linux

If you are familiar with a Linux or UNIX environment, there is no need to take any course. Working with Linux on a supercomputer is not that different from working with Linux on a PC, so you'll likely find your way around quickly.

Otherwise, there are several options to learn more about Linux

A basic HPC introduction

Such a course at the VSC has a double goal: Learning more about HPC in general but also about specific properties of the system at the VSC that you need to know to run programs sufficiently efficiently.

  • Several institutions at the VSC organise periodic introductions to their infrastructure or update sessions for users when new additions are made to the infrastructure. Check the "Education and Training" page on upcoming courses.
  • We are working on a new introductory text that will soon be available on this site. The text covers both the software that you need to install on your own computer and working on the clusters, with specific information for your institution.
  • Or you can work your way through the documentation on the user portal. This is probably sufficient if you are already familiar with supercomputers. Of particular interest may be the page on our implementation of the module system, the pages on running jobs (as there are different job submission systems around, we use Torque/Moab), and the pages about the available hardware that also contain information about the settings needed for each specific system.

What next?

We also run courses on many other aspects of supercomputing such as program development or use of specific applications. As the other courses, they are announced on our "Education and Training" page. Or you can read a some good books, look at training programs offered at the European level through PRACE or check some web courses. We maintain links to several of those on the "Tutorials and books" pages.

Be aware that some tools that are useful to prototype applications on a PC, may be very inefficient when run at a large scale on a supercomputer. Matlab programs can often be accelerated through compiling with the Matlab compiler. R isn't the most efficient tool either. And Python is an excellent "glue language" to get a number of applications or optimised (non-Python) libraries to work together, but shouldn't be used for entire applications that consume a lot of CPU time either. We've got courses on several of those languages where you also learn how to use them efficiently, and you'll also notice that on some clusters there are restrictions on the use of these tools.