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.

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

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/