PERUN Supercomputer – Partitions Overview¶

About PERUN Partitions

PERUN uses two main partitions: CPU and GPU.
Each job submitted to Slurm must specify one of these partitions, unless a default is used.

1. What Are Slurm Partitions?¶

A partition in Slurm represents a group of compute nodes with similar characteristics or usage rules.
Partitions define: - hardware constraints (CPU cores, GPUs, memory) - job limitations (max runtime, max cores, number of nodes) - resource availability and priority - access to specialized hardware (e.g., GPU nodes)

Tip — Always choose the correct partition

CPU workloads should run in the cpu_short or cpu_long partition.
GPU or AI workloads must run in the gpu_short or gpu_long partition.

2. Available PERUN Partitions¶

PERUN defines four partitions split by workload type and time limit:

Partition	Nodes	Time Limit	Max Job Size	GPUs	Purpose
cpu_short	cn01–cn32	2 days	Up to system limits	0	Short CPU HPC workloads
cpu_long	cn01–cn32	4 days	Up to system limits	0	Long-running CPU workloads
gpu_short	gpu01–gpu26 (H200)	2 days	Up to 8 GPUs per node	8 per node	Short AI/ML/GPU workloads
gpu_long	gpu01–gpu26 (H200)	4 days	Up to 8 GPUs per node	8 per node	Long AI/ML/GPU workloads

All nodes are available in both short and long partitions

All CPU nodes (cn01–cn32) and GPU nodes (gpu01–gpu26) are available in both the short and long partition. Choose based on your required walltime.

Example — Selecting a Partition

sbatch -p cpu_short job.sh

sbatch -p gpu_long gpu_job.sh

3. Viewing Partition Information¶

You can inspect partitions with:

Basic Slurm overview¶

sinfo

Summary view¶

sinfo -s

Detailed partition definitions¶

scontrol show partitions

Example sinfo Output Snippet

PARTITION  AVAIL  TIMELIMIT   NODES(A/I/O/T) NODELIST
cpu_short*    up 2-00:00:00        5/27/0/32 cn[01-32]
cpu_long      up 4-00:00:00        5/27/0/32 cn[01-32]
gpu_short     up 2-00:00:00        2/24/0/26 gpu[01-26]
gpu_long      up 4-00:00:00        2/24/0/26 gpu[01-26]
viz           up    8:00:00          0/2/0/2 viz[01-02]

Note

Node status values: - idle → ready to run jobs
- alloc → currently running jobs
- mix → partially allocated
- down/drain → node unavailable

4. Choosing the Right Partition¶

Use the cpu_short or cpu_long partition when:¶

running multi-core CPU jobs
performing scientific simulations
running general HPC workloads

Use cpu_short for jobs under 2 days, cpu_long for jobs up to 4 days.

Use the gpu_short or gpu_long partition when:¶

training machine learning / deep learning models
performing GPU-accelerated workloads (CUDA, PyTorch, TensorFlow)
requiring NVIDIA H200 performance

Use gpu_short for jobs under 2 days, gpu_long for jobs up to 4 days.

Warning — GPU misuse

Jobs without GPU requirements should not run on the GPU partitions.

5. Submitting Jobs to a Partition¶

CPU job example¶

#!/bin/bash
#SBATCH -p cpu_short
#SBATCH -n 32
#SBATCH -t 24:00:00
python simulation.py

GPU job example¶

#!/bin/bash
#SBATCH -p gpu_short
#SBATCH --gres=gpu:4
#SBATCH -t 48:00:00
python train.py

Important

Failing to specify --gres=gpu:<num> in the GPU partition will result in no GPUs being allocated.

6. Walltime and Efficiency¶

Why walltime matters¶

Jobs with too-high time limits wait longer in the queue.
Shorter jobs are often scheduled earlier.
Improper walltime estimates decrease cluster efficiency.

Efficient walltime use

If your job usually finishes in 3 hours, do not request 24 hours.
If your job runs under 2 days, prefer cpu_short or gpu_short over the long partitions.

7. Summary¶

PERUN provides four Slurm partitions: cpu_short, cpu_long, gpu_short, gpu_long.
All CPU nodes (cn01–cn32) are available in both cpu_short (2-day limit) and cpu_long (4-day limit).
All GPU nodes (gpu01–gpu26) are available in both gpu_short (2-day limit) and gpu_long (4-day limit).
Correct partition selection improves job scheduling and cluster efficiency.
Use sinfo and scontrol to inspect resources.
Always specify GPUs explicitly when using the gpu partitions.