Next Steps and Resources

Last updated on 2026-08-27 | Edit this page

Overview

Questions

  • What other workshops are available?
  • Where do I go for help?
  • How do I handle more complex job patterns?

Objectives

  • Know where to find additional HPC training resources
  • Understand common next-step workflows beyond basic job submission
  • Know how to get help from the HPC team

Beyond Your First Job

Now that you can connect, navigate files, load modules, and submit jobs, here are common next steps:

Interactive Jobs for Development

Use srun for interactive sessions on compute nodes:

BASH 

srun --partition=short --time=01:00:00 --cpus-per-task=4 --mem=8G --pty bash

This gives you a bash shell on a compute node where you can test code interactively without running on the head node.

A long terminal transcript. The batch script is shown with cat, an srun command with a ten-minute limit opens an interactive shell and the prompt changes to node a001, sbatch submits the job from there, ls shows the numbered log file, cat prints Fibonacci 1 through 20 ending in 6765 and Done, and exit returns to the head node prompt.
A full edit-test cycle in an interactive session: the prompt changes from sagehen to compute node a001 after srun --pty bash.

Email Notifications

Get notified when your job finishes:

BASH 

#SBATCH --mail-type=END,FAIL
#SBATCH --mail-user=username@pomona.edu
A syntax-highlighted batch script in a text editor. The SBATCH header for the fibonacci job on the short partition now includes mail-type END,FAIL and a mail-user line, above the module purge and miniconda3 load lines and the python command.
The Fibonacci batch script with the two mail directives added (students’ addresses are on the mymail.pomona.edu domain).
An email client showing a message from ITS High Performance Computing. The subject reads Slurm Job_id 37651, Name fibonacci, Ended, Run time zero seconds, COMPLETED, Exit Code 0. The message body is empty.
The email SLURM sends when the job ends — the subject line carries the job ID, name, run time, state, and exit code. Note it comes from the its-hpcmbx mailbox; don’t reply to it.

Customizing Your Shell

Edit ~/.bashrc to set up your environment on every login:

BASH 

nano ~/.bashrc

Useful additions:

BASH 

# Helpful aliases
alias ll='ls -lah'
alias sq='squeue -u $USER'
alias sa='sacct -u $USER'

# Set your default editor
export EDITOR=nano

Reload with source ~/.bashrc.

Additional Workshop Topics

The Pomona College HPC Workshop Series covers many advanced topics:

  • SLURM Job Scheduling: Advanced batch scripting, job arrays, dependencies, GPU workflows
  • Data Transfer and Management: Moving data to/from Sagehen HPC with scp, rsync, Globus
  • Security and Access: SSH keys, data classification, encrypted storage
  • OnDemand Portal: Graphical access, Jupyter, RStudio on the cluster
  • Domain-Specific Workshops: R, Python, bioinformatics, and more

Getting Help

HPC Support

  • Email:
  • Include in your email:
    • Your username
    • Job ID (if applicable)
    • Error messages or output
    • What you expected vs. what happened

Common Self-Help Commands

BASH 

# Check cluster status
sinfo

# Check your running/pending jobs
squeue -u $USER

# Check completed job details
sacct -j JOBID --format=JobID,JobName,State,Elapsed,MaxRSS

# Check your storage quota
quota_check.sh

# See what modules are loaded
module list
Callout

Quick Reference Card

Task Command
Connect ssh <myusername>@sagehen.hpc.pomona.edu
Submit job sbatch script.sh
Check jobs squeue -u $USER
Job history sacct -u $USER
Cancel job scancel JOBID
Cluster status sinfo
Load software module load package
Reset modules module purge
Check quota quota_check.sh
OnDemand https://ondemand.hpc.pomona.edu
Challenge

Challenge 1: Plan Your First Real Project

Think about a research computation you want to run on Sagehen. Write down a plan covering:

  1. What software/modules do you need?
  2. How much memory and how many cores?
  3. How long will it take?
  4. Which partition should you use?
  5. What input data do you need to upload?
  6. Where should output be saved?

Draft a skeleton batch script for this project (just the SBATCH headers and module loads; the actual command can be a placeholder).

A good plan should include:

BASH 

#!/bin/bash
#SBATCH --job-name=my_research
#SBATCH --partition=amd          # or `gpu` for GPU jobs, `short` for quick tests
#SBATCH --time=08:00:00          # realistic estimate
#SBATCH --cpus-per-task=16       # based on your code's parallelism
#SBATCH --mem=64G                # based on data size
#SBATCH --output=%j_output.log

module purge
module load miniconda3/py313_26.3.2-2   # or whatever you need (check module avail)

# Your research command here
python my_analysis.py --input /bigdata/lab/<labname>/data --output ~/results/

Key considerations: - Start with a short test on the short partition (small --time, e.g., 10–30 minutes) before launching production work on amd or gpu - Scale up resources once you know what your code needs - Save important output to /rhome or /bigdata, not /scratch

Key Points
  • 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 for help; include your username, job ID, and error messages
  • Use quota_check.sh (not du) to check BeeGFS storage usage