Software stack#

Software installation and maintenance on HPC infrastructure such as the VSC clusters poses a number of challenges not encountered on a workstation or a departmental cluster. For many libraries and programs, multiple versions have to installed and maintained as some users require specific versions of those. In turn, those libraries or executables sometimes rely on specific versions of other libraries, further complicating the matter.

The way Linux finds the right executable for a command, and a program loads the right version of a library or a plug-in, is through so-called environment variables. These can, e.g., be set in your shell configuration files (e.g., .bashrc), but this requires a certain level of expertise. Moreover, getting those variables right is tricky and requires knowledge of where all files are on the cluster. Having to manage all this by hand is clearly not an option.

We deal with this on the VSC clusters in the following way. First, we’ve defined the concept of a toolchain. They consist of a set of compilers, MPI library and basic libraries that work together well with each other, and then a number of applications and other libraries compiled with that set of tools and thus often dependent on those. We use tool chains based on the Intel and GNU compilers, and refresh them twice a year, leading to version numbers like 2014a, 2014b or 2015a for the first and second refresh of a given year. Some tools are installed outside a toolchain, e.g., additional versions requested by a small group of users for specific experiments, or tools that only depend on basic system libraries. Second, we use the module system to manage the environment variables and all dependencies and possible conflicts between various programs and libraries, and that is what this page focuses on.

Using the module system#

Many software packages are installed as modules. These packages include compilers, interpreters, mathematical software such as Matlab and SAS, as well as other applications and libraries. This is managed with the module command.

Available modules#

To view a list of available software packages, use the command module av. The output will look similar to this:

$ module av
----- /apps/leuven/skylake/2018a/modules/all ------

Module names#

In general, the anatomy of a module name is


For example for Boost/1.66.0-intel-2018a-Python-3.6.4, we have

  • <package>: Boost, the name of the library,

  • <version>: 1.66.0, the version of the Boost library,

  • <toolchain>: intel-2018a, the toolchain Boost was built with, and

  • <extra>: Python-3.6.4, the version of Python this Boost version can inter-operate with.

Some packages in the list above include intel-2014a or foss-2014a in their name. These are packages installed with the 2014a versions of the toolchains based on the Intel and GNU compilers respectively. The other packages do not belong to a particular toolchain. The name of the packages also includes a version number (right after the /) and sometimes other packages they need.

Searching modules#

Often, when looking for some specific software, you will want to filter the list of available modules, since it tends to be rather large. The module command writes its output to standard error, rather than standard output, which is somewhat confusing when using pipes to filter. The following command would show only the modules that have the string ‘python’ in their name, regardless of the case.

$ module av |& grep -i python

For more comprehensive searches, you can use module spider, e.g.,

$ module spider python

Info on modules#

The spider sub-command can also be used to provide information on on modules, e.g.,

$ module spider Python/2.7.14-foss-2018a

  Python: Python/2.7.14-foss-2018a
        Python is a programming language that lets you work more
        quickly and integrate your systems more effectively.

    This module can be loaded directly: module load Python/2.7.14-foss-2018a

More technical information can be obtained using the show sub-command, e.g.,

$ module show Python/2.7.14-foss-2018a

Loading modules#

A module is loaded using the command module load with the name of the package, e.g., with the above list of modules,

$ module load BEAST

will load the BEAST/2.1.2 package.

For some packages, e.g., zlib in the above list, multiple versions are installed; the module load command will automatically choose the lexicographically last, which is typically, but not always, the most recent version. In the above example,

$ module load zlib

will load the module zlib/1.2.8-intel-2014a. This may not be the module that you want if you’re using the GNU compilers. In that case, the user should specify a particular version, e.g.,

$ module load zlib/1.2.8-foss-2014a


Loading modules with explicit versions is considered best practice. It ensures that your scripts will use the expected version of the software, regardless of newly installed software. Failing to do this may jeopardize the reproducibility of your results!

Modules need not be loaded one by one; the two ‘load’ commands can be combined as follows:

$ module load  BEAST/2.1.2  zlib/1.2.8-foss-2014a

This will load the two modules and, automatically, the respective toolchains with just one command.


Do not load modules in your .bashrc, .bash_profile or .profile, you will shoot yourself in the foot at some point. Consider using module collections restore as a command line alternative (so not in the shell initialization files either!).

List loaded modules#

Obviously, the user needs to keep track of the modules that are currently loaded. After executing the above two load commands, the list of loaded modules will be very similar to:

$ module list
Currently Loaded Modulefiles:
  1) /thinking/2014a
  2) Java/1.7.0_51
  3) icc/2013.5.192
  4) ifort/2013.5.192
  5) impi/
  6) imkl/
  7) intel/2014a
  8) beagle-lib/20140304-intel-2014a
  9) BEAST/2.1.2
 10) GCC/4.8.2
 11) OpenMPI/1.6.5-GCC-4.8.2
 12) gompi/2014a
 13) OpenBLAS/0.2.8-gompi-2014a-LAPACK-3.5.0
 14) FFTW/3.3.3-gompi-2014a
 15) ScaLAPACK/2.0.2-gompi-2014a-OpenBLAS-0.2.8-LAPACK-3.5.0
 16) foss/2014a
 17) zlib/1.2.8-foss-2014a

It is important to note at this point that, e.g., icc/2013.5.192 is also listed, although it was not loaded explicitly by the user. This is because BEAST/2.1.2 depends on it, and the system administrator specified that the intel toolchain module that contains this compiler should be loaded whenever the BEAST module is loaded. There are advantages and disadvantages to this, so be aware of automatically loaded modules whenever things go wrong: they may have something to do with it!

Unloading modules#

To unload a module, one can use the module unload command. It works consistently with the load command, and reverses the latter’s effect. One can however unload automatically loaded modules manually, to debug some problem.

$ module unload BEAST

Notice that the version was not specified: the module system is sufficiently clever to figure out what the user intends. However, checking the list of currently loaded modules is always a good idea, just to make sure…

Purging modules#

In order to unload all modules at once, and hence be sure to start with a clean slate, use:

$ module purge


It is a good habit to use this command in PBS scripts, prior to loading the modules specifically needed by applications in that job script. This ensures that no version conflicts occur if the user loads module using his .bashrc file.

Getting help#

To get a list of all available module commands, type:

$ module help

Collections of modules#

Although it is convenient to set up your working environment by loading modules in your .bashrc or .profile file, this is error prone and you will end up shooting yourself in the foot at some point.

The module system provides an alternative approach that lets you set up an environment with a single command, offering a viable alternative to polluting your .bashrc.

Define an environment

  1. Be sure to start with a clean environment

    $ module purge
  2. Load the modules you want in your environment, e.g.,

    $ module load matplotlib/2.1.2-intel-2018a-Python-3.6.4
    $ module load matlab/R2019a
  3. save your environment, e.g., as data_analysis

    $ module save data_analysis

Use an environment

$ module restore data_analysis

List all your environments

$ module savelist

Remove an environment

$ rm ~/.lmod.d/data_analysis

Specialized software stacks#

The list of software available on a particular cluster can be unwieldingly long and the information that module av produces overwhelming. Therefore the administrators may have chosen to only show the most relevant packages by default, and not show, e.g., packages that aim at a different cluster, a particular node type or a less complete toolchain. Those additional packages can then be enabled by loading another module first. E.g., to get access to the modules in the (at the time of writing) incomplete 2019a toolchain on UAntwerpen’s leibniz cluster, one should first enter

$ module load leibniz/2019a-experimental