Key Points

What is High-Performance Computing?

High-performance computing enables research that would be infeasible on personal computers
HPC clusters combine many powerful nodes with fast networks and shared storage
Sagehen is Pomona College’s HPC cluster, available at no cost to researchers
SLURM is the job scheduler that manages fair resource allocation among all users

Use HPC when your data is large, your computation is long, or you need specialized hardware
Sagehen has 12 compute nodes (128 cores, 500 GB each) and multiple GPU nodes
The head node is for login and job submission only; never run jobs there
Different storage locations serve different purposes: home, lab, scratch, tmpfs
Contact its-hpc@pomona.edu for help or to request higher resource limits

SSH (Secure Shell) is the standard for securely connecting to remote computers
macOS and Linux include SSH built-in; Windows users can use PowerShell or PuTTY
DUO two-factor authentication is required; push notification is fastest
OnDemand (https://ondemand.hpc.pomona.edu/) provides a graphical alternative to SSH
Always log out when done with exit or Ctrl+D

Use mkdir -p to create nested directories in one command
Be extremely careful with rm -rf; always list contents first
Organize work into logical directories: projects, code, data, results
File permissions control read, write, and execute access for owner, group, and others
Use chmod to change permissions; share files via /bigdata or by adjusting group permissions

Partitions group nodes by hardware type and enforce time/resource limits
Sagehen has three partitions: amd (general purpose), gpu (GPU-accelerated), and short (quick / test / debug jobs with a shorter walltime)
Each compute node has 128 AMD EPYC cores and 500 GB RAM
GPU partition offers 10 GPUs: A100, L40S, and RTX PRO 6000
Use sinfo to check partition status, node states, and available resources
Maximum job time: 720 hours (30 days) on amd and gpu; check sinfo -p short for the short partition limit

Each Sagehen node has 128 cores and 500 GB RAM; SLURM allocates whole cores
SLURM finds the best-fit nodes for your resource request automatically
Use gpu for GPU-accelerated work, short for quick test / debug jobs (shorter walltime), and amd for general CPU production work
Request only the resources you need to avoid wasting shared capacity
Contact its-hpc@pomona.edu if you need limits increased for a project

The module system solves software version conflicts on shared HPC clusters
Sagehen uses Lmod with 90+ pre-installed packages
Use module avail to search, module load to activate, module purge to reset
Only one version of each module family can be loaded at a time
Always use module purge at the start of job scripts for reproducibility
Request new software at its-hpc@pomona.edu

Batch jobs are scripts submitted with sbatch; they run when resources are available
Every batch script needs #!/bin/bash and SBATCH directives for resources
Key directives: –partition, –time, –cpus-per-task, –mem, –output
Monitor jobs with squeue -u $USER (running) or sacct (history)
Always use module purge in job scripts for reproducibility
Test on the short partition before submitting long production jobs to amd or gpu
Common failures: TIMEOUT (increase –time), OUT_OF_MEMORY (increase –mem)

Use srun --pty bash for interactive compute sessions instead of running on the head node
Email notifications keep you informed about job completion and failures
Customize ~/.bashrc for a more productive login environment
Additional workshops cover SLURM advanced features, data transfer, security, and more
Contact its-hpc@pomona.edu for help; include your username, job ID, and error messages
Use quota_check.sh (not du) to check BeeGFS storage usage