General supercomputer-related questions

No. There’s also a need for an in-depth support which is out-of-scope for EPICURE if the problem lies in the core functions of the code.

Level 2 and Level 3 support.

Level 1 support.

Examples

LUMI: Go to https://my.lumi-supercomputer.eu/login/ > Project (left menu bar) > (project name) > team > Add. Add the email address the EPICURE team members gave you.

Karolina: All users must have an active IT4I account. Once their account is created, you can add them to your project using their login name: https://docs.it4i.cz/en/docs/general/get-project/applying-for-resources#authorize-collaborators-for-your-project . If you have any issues, please contact user support at: support[at]it4i.cz

You can usually get support within a few days after submitting a request. The process is straightforward: once your request is accepted, an initial meeting will be scheduled to discuss your needs and determine the next steps for support. You can submit your support request directly at https://eurohpcsupport.eu/apply-now/. All additional information about EPICURE and how to request support can be found at https://epicure-hpc.eu/.

Yes, you can. For documentation on using Vega, visit Vega Docs (https://en-vegadocs.vega.izum.si/ ). If you need additional support or access on Vega, you can contact the team at support@sling.si.

Yes. Installing user-level software is common and supported on HPC systems, as it is not possible to pre-install or provide every tool or application that users may require.

Therefore, there is a best practice for this:

  • Check the availability of the required software
    • Run ‘module avail’ or ‘module spider’, review the documentation or ask colleagues/HPC support.
    • If they are available, try to use them first, as the centrally provided versions are typically better configured and supported.
  • Install only in approved locations that you control
    • Check the documentation for your system to find out what is shared and what is backed up.
    • $HOME space is typically suited to small, non-shared installations.
    • $PROJECT/$GROUP space is typically suited to shared software.
    • $SCRATCH/$WORK space for large, temporary builds.
  • Check that no root access is required (e.g. it can be installed in a user space, no access to hardware counters, etc.).
  • Use the appropriate package managers
    • ‘pip’ works well when used with virtual environments or with the ‘–user’ install option.
    • mircomamba / mini-forge / etc.
    • Make sure to install environments in your own directory.
    • Check the HPC documentation for any restrictions or preferred Python environment setups.
  • Respect licensing requirements
    • Ensure that you are licensed to install/use the software.
    • Verify that licence servers are reachable from compute nodes, if needed.
  • Organise and manage your environment.
    • Maintain a clear directory structure.
    • Provide module files for easy loading and version management.
  • Use HPC-friendly packaging tools where possible
    • Spack and EasyBuild simplify complex dependency management.
    • They help to ensure portability and reproducibility across teams.

If you are unsure about the location, licensing or best practice, contact HPC (or EPICURE) support for assistance. They may even integrate your successfully installed software into the module system for shared use.

On LUMI, you should provide the license for the additional software you want to use.

When you are allocated a project on an HPC system, you are granted specific quotas that determine how many resources you can use.

The first of these is compute time, which is often expressed as core-hours (node-hours/GPU-hours). Some HPC centres allow you to continue submitting jobs after the allocation has been used up, but with a reduced priority; others block submissions. Some centres also define how compute time may be consumed, e.g. on some HPC systems you can use the entire allocation at any time during the project period; on others you are expected to use resources linearly throughout the project time to ensure a fair and balanced system load. You should therefore always check the local policy to avoid unexpected interruptions in your work.

In addition to compute time quotas, you will have storage quotas, which are set differently on each filesystem. If you exceed the quota for any filesystem, you may be unable to create new files or directories until usage is reduced.

Finally, each filesystem also has an inode quota, which limits the number of files and directories that can be stored. Even if you have free storage capacity, you cannot create additional files once the inode limit is reached. Inode limits can be just as restrictive as storage size.

If you anticipate any violations of quota policies (e.g. large spikes in workload, significant increase in storage requirements, or workflows that generate many small files), contact HPC Support well in advance, as quota adjustments often require approval and planning.

Examples

MareNostrum 5:

Use this command to get information about all your accounts

module load bsc

bsc_acct

Use this command to get the quota information for all your groups/accounts.

bsc_quota

Deucalion

Run the ‘billing’ command to see how many hours you have left in your account. To get usage per user for a specific account, run billing -a <your account>.

For a supercomputer, we’d recommend python venv in most cases. Here’s why:

Advantages of venv on supercomputers:

  • Faster and lighter – No heavy dependency solver, minimal overhead
  • Better compatibility – Uses the system’s optimised Python builds that HPC staff have compiled with performance flags
  • Simpler – Easier for sysadmins to support and troubleshoot
  • Module system integration – Works well with environment modules (Lmod/TCL modules) that most HPC systems use
  • No conda conflicts – Avoids issues where conda’s packages override carefully-tuned system libraries

When conda might be better:

  • You need non-Python dependencies (like CUDA libraries, compilers) that aren’t available via modules
  • You’re using complex scientific stacks where conda handles tricky dependency chains (e.g., some bioinformatics workflows)
  • You need exact reproducibility across different systems

Best practice on HPC:

# Load optimized Python from modules
module load python/3.11

# Create venv
python –m venv myenv
source myenv/bin/activate

# Install what you need
pip install numpy scipy torch

Many supercomputers explicitly recommend against conda because it can interfere with their optimised software stack. Check your centre’s documentation first – they often have specific guidance and may provide pre-built environments for common frameworks.

If your question concerns EPICURE support or European HPC Application Support Portal, please contact pmo-epicure@postit.csc.fi.

If you have a specific question about our HPC systems see the following websites or contact the support services of the HPC system in question:

Deucalion: https://docs.macc.fccn.pt/

Discoverer: https://docs.discoverer.bg/resource_overview.html

Jupiter: https://www.fz-juelich.de/en/jsc/jupiter/faq

Karolina: https://docs.it4i.cz/en/docs/general/support 

Leonardo: https://docs.hpc.cineca.it/faq.html

LUMI: https://www.lumi-supercomputer.eu/faq/

MareNostrum 5: https://www.bsc.es/supportkc/FAQ

MeluXina: https://docs.lxp.lu/FAQ/faq/

Vega: https://en-vegadocs.vega.izum.si/faq/