Research computing on HPC clusters

HPC clusters are where much research code actually runs, and they invert desktop habits: you do not run programs, you request resources and submit batch jobs; you do not install software, you load modules or bring containers; the login node is a shared hallway, not a workstation. An agent's job is to translate the user's computation into this model correctly and reproducibly - and to respect that every cluster has local documentation that overrides generic advice.

Batch jobs done right (SLURM)

A job script is a shell script with resource directives:

#!/bin/bash
#SBATCH --job-name=fit-model
#SBATCH --time=02:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=16G
#SBATCH --output=logs/%x-%j.out

module load Python/3.12
srun python fit_model.py --config configs/run.yaml

Practices that matter:

• Request honestly: measure a pilot run, then set time/memory with modest headroom. Over-requesting wastes allocation and queue priority (and energy - rseng-green-computing); under-requesting kills jobs at 99%.
• Never compute on the login node; test with a short interactive allocation (srun --pty or salloc) instead.
• Parameter sweeps are job arrays (--array=0-99), not 100 submitted scripts; index into a config list with $SLURM_ARRAY_TASK_ID (externalized run configs pair naturally with rseng-workflows).
• Long jobs checkpoint: clusters preempt and nodes fail; save restartable state at intervals and make the script resume from the last checkpoint.
• Log usefully: %x-%j names, and seff JOBID after completion to compare requested vs used resources - feed that back into the next request.

Software environments on clusters

Three layers, in order of preference for reproducibility:

1. Containers with Apptainer (formerly Singularity): the HPC-native container runtime - unprivileged, image-file based, GPU-aware. Build once (possibly from a Docker image), run identically on any cluster; pairs with rseng-reproducible-environments for the build recipe.
2. EESSI: a shared, optimized, ready-to-use scientific software stack streamed to any cluster or laptop - when available it gives identical software everywhere without building anything.
3. Module system (module avail / module load): the site's curated builds; record exact module versions in the job script - an unversioned module load Python is a reproducibility hole.

Never pip-install into the home directory as the primary strategy; quota, node-architecture and reproducibility problems follow.

Parallelism: pick the right kind

• Independent runs (sweeps, bootstraps, per-file processing): job arrays or a workflow engine - no MPI needed. This covers most research workloads.
• Single-node parallelism: threads/multiprocessing; --cpus-per-task, and pin BLAS/OpenMP thread counts to match.
• Multi-node tightly-coupled computation: MPI (mpi4py or native), launched with srun; requires the code to be written for it. Distributed data processing frameworks are their own path (rseng-big-data-processing).

Scaling discipline: measure strong/weak scaling on small counts before requesting large allocations; doubling cores that yield 1.2x wastes everyone's allocation (rseng-performance-profiling for the measurement).

Cluster citizenship and data

• Filesystems differ: home (small, backed up), project (shared), scratch (fast, purged) - stage data to scratch for jobs, move results back, and script the staging (rseng-data-management).
• Transfer with rsync (resumable) or the site's data-transfer nodes; never leave the only copy of results on scratch.
• Read the site's own docs first; when the user's cluster is known, prefer its conventions over anything generic, including this skill.

Working with this skill

This skill is source-independent: its authority is the scheduler, container and software-stack documentation linked below, plus each cluster's local documentation.

Learn more (verified):

• https://slurm.schedmd.com - SLURM documentation
• https://apptainer.org - Apptainer container runtime
• https://www.eessi.io/docs/ - EESSI shared software stack
• https://carpentries-incubator.github.io/hpc-intro/ - HPC Carpentry introduction
• https://coderefinery.github.io/TTT4HPC_parallel_workflows/ - CodeRefinery tuesday-tools lessons on HPC workflows

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Related skills

Check whether any of these applies before moving on:

• rseng-big-data-processing - distributed data framework path
• rseng-data-management - staging data across cluster filesystems
• rseng-green-computing - honest requests save energy
• rseng-performance-profiling - measure scaling before allocating
• rseng-reproducible-environments - containers and pinned modules
• rseng-workflows - sweeps via workflow engines

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