GPU Computing on Sagehen HPC

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Pomona College HPC Workshop Series

This workshop is part of the Pomona College Research Computing Workshop Series, designed for GPU computing on the Sagehen HPC cluster.

Cluster: sagehen.hpc.pomona.edu Web Portal: OnDemand Support: its-hpc@pomona.edu

Sagehen provides NVIDIA A100, L40S, and RTX PRO 6000 GPUs for deep learning, scientific computing, and data analysis workflows.

Developed for Pomona College by Andrew Wilson, ITS Research Computing. Licensed under CC-BY 4.0.

GPU Computing on Sagehen

Welcome to this workshop! Check the episodes to get started.

Acknowledgments

Developed by Andrew Wilson, Director of Research Computing and Digital Scholarship at Pomona College, with Andrei Motchenko, who tested, edited and produced screenshots for the workshop series.

Prerequisites

Before attending this workshop, you should:

Have an active Sagehen HPC account. If you do not yet have one, request access via the HPC account request form (or by emailing its-hpc@pomona.edu). Allow 1–2 business days for account provisioning.
Complete the Introduction to HPC Systems workshop (Workshop 0) or have equivalent experience with basic Linux commands, SSH access, and SLURM job submission on Sagehen.
Complete the SLURM Job Scheduling workshop (Workshop 9) or be comfortable writing and submitting batch scripts with sbatch.
Be familiar with at least one programming language commonly used in GPU computing (Python, C/C++, or CUDA).

Accessing Sagehen

You can access the Sagehen cluster in two ways:

Option 1: SSH (Terminal Access)

BASH

ssh <myusername>@sagehen.hpc.pomona.edu

You will be prompted for your Pomona College credentials and DUO Multi-Factor Authentication.

Option 2: OnDemand Portal (Browser Access)

Navigate to https://ondemand.hpc.pomona.edu/ and log in with your Pomona credentials. OnDemand provides a web-based terminal, file manager, and JupyterLab for interactive GPU work.

GPU Resources on Sagehen

Sagehen has 10 GPUs across the cluster (confirmed May 2026; see README.md for hardware breakdown) in the gpu partition with the following accelerators:

GPU Model	VRAM	Quantity	Best For
NVIDIA A100	80 GB	4	Large deep learning models, mixed-precision training
NVIDIA L40S	48 GB	4	Inference, visualization, moderate training
NVIDIA RTX PRO 6000	96 GB	2	Large memory workloads, professional rendering, mid-large model training

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Account Limits

Each account/group is limited to a maximum of 4 GPUs simultaneously. Plan your experiments accordingly and release GPU resources when you are done.

Loading GPU Software

Sagehen uses the Lmod module system to manage software. To set up a GPU computing environment:

BASH

# Check available CUDA versions
module spider cuda

# Load CUDA toolkit
module load cuda

# Load Python with GPU support (PyTorch, TensorFlow)
module load miniconda3

# Verify GPU access (on a GPU node only)
nvidia-smi

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Important: GPUs Are Only on Compute Nodes

The head node (login node) does not have GPUs. You must request a GPU node via SLURM before running nvidia-smi or any GPU code. Use an interactive session for testing:

BASH

srun --partition=gpu --gres=gpu:1 --time=01:00:00 --pty bash

Pre-Workshop Checklist

Before the workshop begins, please verify the following:

You can SSH into sagehen.hpc.pomona.edu or log into OnDemand
You can load the CUDA module: module load cuda
You can load miniconda3: module load miniconda3
You understand how to submit a basic SLURM batch job

Getting Help

If you encounter any issues during setup, contact the HPC team:

Email: its-hpc@pomona.edu
Response time: Standard requests are addressed within 1–2 business days