Skip to main content

Installed software

  • System
    • Operating system : RHEL 9.1
    • Job scheduler : Slurm 22.05.9
    • Parallel file system : BeeGFS 7.4.1

The following compilers, applications, tools and libraries are available as environment modules.

info

If the software you need is not available or if you have specific needs, let us know at hpc@univ-lille.fr. We will help you find an appropriate solution.

Compilers​

GNU​

  • gcc/11.3.1
  • gcc/11.3.1/openmpi/4.1.5
  • gcc/11.3.1/openmpi/5.0.1

Intel​

  • intel/2024/compilers
  • intel/2024/openmpi/5.0.1

Nvidia​

  • nvidia/cuda/12.0/compilers
  • nvidia/cudnn/8.9.6
  • nvidia/nvhpc
    • Loading this module will make several nvhpc (sub)modules available

PGI​

  • pgi/19.10/compilers
  • pgi/19.10/compilers-nollvm
  • pgi/19.10/openmpi/3.1.3

Libraries​

  • boost/1.82/gcc/11.3.1
  • fftw/3.3.10
  • fftw/3.3.10.mpi
  • hdf5/1.14.3/intel/2024/mpi
  • hdf5/1.14.3/intel/2024/seq
  • hdf5/1.14.3/nvhpc/24.1.mpi
  • netcdf/4.9.2/mpi
  • netcdf/4.9.2/seq
  • petsc/3.20

Logiciels scientifiques​

  • amber
  • cp2k
  • crest
  • diann
  • dlpoly
  • freefem++
  • gromacs
  • lammps
  • matlab
  • molden
  • molpro
  • n2p2
  • namd
  • openfoam
  • phonopy
  • plumed
  • polyrate
  • pynx
  • pytorch
  • quantum espresso
  • R
  • uspex
  • vaspkit
  • wannier90
  • xtb
caution

The following codes are only available to users with a valid license.

  • abaqus
  • ansys
  • gaussian
  • schrodinger
  • vasp

Python environments​

  • conda/23.11-py311

We recommend to use conda virtual environments for your python projects.

Once the conda module is loaded, the following conda environments are available :

  • base
  • python311-tensorflow
    • Tensorflow 2.15
  • python311-tools
    • Environment with frequently used tools : scipy numpy numba pandas dask matplotlib pytorch keras scikit-learn seaborn beautifulsoup4 zeromq
  • scikit-learn
    • scikit-learn 1.4.1
  • pytorch-gpu
    • pytorch 2.2.1 with GPU support
  • tf-gpu
    • Tensorflow 2.15 with GPU support

Example:

# load conda module  
ml conda/23.11-py311
# list available environments
conda info --envs
# activate python311-tensorflow
conda activate python311-tensorflow
# check python version
python --version
# check that tensorflow works
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
# deactivate environment
conda deactivate