wICE quick start guide#

wICE is the most recent KU Leuven/UHasselt Tier-2 cluster. It can be used for most workloads, and has nodes with a lot of memory, as well as nodes with GPUs.

wICE does not have separate login nodes and can be accessed either from the Genius login nodes, or from your web browser via the Open On-Demand service.

Running jobs on wICE#

There are several type of nodes in the wICE cluster: normal compute nodes, GPU nodes, big memory nodes and nodes configured for interactive use. The resource specifications for jobs have to be tuned to use these nodes properly.

In general, the maximum walltime for wICE jobs is 3 days (72 hours). Only jobs submitted to the *_long partitions are allowed to have walltimes up to 7 days (168 hours), as will be illustrated below.

Similar to Genius, wICE uses Slurm as the workload manager. A Slurm jobscript for wICE will typically look like this:

#!/bin/bash -l
#SBATCH --clusters=wice
#SBATCH --partition=...
#SBATCH --time=...
#SBATCH --nodes=...
#SBATCH --ntasks-per-node=...
#SBATCH --account=...

module load ...


In case you are not yet familiar with Slurm and/or the wICE hardware, you can find more information on the following pages:

For information about using and installing software on wICE (including Conda environments), see the advanced guide for wICE.

For information about compute credit accounts, see Leuven accounting and KU Leuven credits pages.

Submit to a regular compute node#

Submitting a 2-node job on the regular IceLake compute nodes (with 72 cores each) can be done like this:

$ sbatch --account=lp_myproject --clusters=wice \
         --nodes=2 --ntasks-per-node=72 --time=2:00:00 myjobscript.slurm

This will select the default partition (called batch) which is the one containing these IceLake nodes. The batch_icelake partition is equivalent to the batch one.

To use Sapphire Rapids nodes instead (mind the higher core count):

$ sbatch --account=lp_myproject --clusters=wice --partition=batch_sapphirerapids \
         --nodes=2 --ntasks-per-node=96 --time=2:00:00 myjobscript.slurm

For jobs which need more than 3 days of walltime (up to maximum 7 days), you need to submit to the respective batch_long/batch_icelake_long or batch_sapphirerapids_long partitions instead.

Submit to the interactive partition#

There is also a small interactive partition intended for interactive work (compiling software, post-processing, small-scale debugging, visualization, …). This is typically done via interactive jobs, for example:

# A short single-core job:
$ srun --account=lp_myproject --clusters=wice --partition=interactive \
        --ntasks=1 --time=01:00:00 --pty bash -l

# A longer job with more cores, a GPU instance and X11 forwarding:
$ srun --account=lp_myproject --clusters=wice --partition=interactive \
       --ntasks-per-node=8 --gpus-per-node=1 --time=08:00:00 --x11 --pty bash -l

Users are allowed to request a maximum of 8 cores, one A100 GPU instance (equal to 1/7th of the physical device), for walltimes up to 16 hours.


It is also possible to submit interactive jobs to the other partitions (e.g. batch, gpu or bigmem) in case you need more resources. For large amounts of compute resources, however, we recommend to use batch jobs since these will result in fewer idling resources compared to interactive jobs.

Submit to nodes with more memory#

IceLake nodes with 2 TiB of RAM are placed in the bigmem partition. To select the maximum amount of memory per core (28 000 MB, which is also the default), you can submit a job as follows:

$ sbatch --account=lp_myproject --clusters=wice --partition=bigmem \
         --nodes=2 --ntasks-per-node=72 --mem-per-cpu=28000M myjobscript.slurm

There is also one IceLake node with even more memory (8 TiB RAM) in the hugemem partition (defaulting to --mem-per-cpu=111900M). In contrast to Superdome, you do not need to request entire sockets and so the node is more similar to the other large memory nodes in this regard.

Submit to a GPU node#

The nodes with A100 GPUs are located in the gpu_a100 partition (the gpu partition also covers the same nodes). As for the other node types, the GPU nodes can be shared by different jobs from different users but each job has exclusive access to its allocated cores and GPU(s).

If you e.g. need one A100 GPU:

$ sbatch --account=lp_myproject --clusters=wice --partition=gpu_a100 \
         --nodes=1 --ntasks=18 --gpus-per-node=1 myjobscript.slurm

We recommend to request 18 cores for every GPU, so an example for 3 GPUs would look like this:

$ sbatch --account=lp_myproject --clusters=wice --partition=gpu_a100 \
         --nodes=1 --ntasks=54 --gpus-per-node=3 myjobscript.slurm

There are also nodes with H100 GPUs and AMD Genoa CPUs (4 GPUs and 64 cores per node) which you can select via the gpu_h100 partition, e.g.:

$ sbatch --account=lp_myproject --clusters=wice --partition=gpu_h100 \
         --nodes=1 --ntasks=16 --gpus-per-node=1 myjobscript.slurm

For easier development and testing with a full GPU, also a gpu_a100_debug partition is available which accepts jobs with walltimes up to 30 minutes, e.g.:

$ sbatch --account=lp_myproject --clusters=wice --partition=gpu_a100_debug \
         --nodes=1 --ntasks=64 --gpus-per-node=1 --walltime=00:10:00 \

The node in this partition is of the same type as those in the interactive partition except that its A100 GPU is not divided into smaller instances.