Welcome to RCAC Documentation¶

Announcement

This is a demo site. You are visiting a demo site designed for testing purposes only. Contents on this website may not reflect production RCAC resources. Check rcac.purdue.edu for official information.

New to RCAC?¶

Follow these steps to get up and running on RCAC clusters.

Get an Account

Request access to RCAC computing resources through your Purdue career account or an ACCESS account.

Purdue account ACCESS account
Connect to a RCAC Cluster

Learn how to log in via SSH, set up your environment, and access the cluster for the first time.

Connection guide
Transfer Your Data

Move files to and from the cluster using SCP, SFTP, Globus, or the research data depot.

Data transfer
Submit Your First Job

Write a Slurm batch script, submit it to the scheduler, and monitor your job's progress.

Job submission guide

HPC User Guides¶

Anvil

NSF-funded capacity cluster for the national research community. Features AMD EPYC Milan CPUs, NVIDIA A100 GPUs, and large-memory nodes. Available through ACCESS allocations.

128 cores/node | 256 GB RAM | A100 40GB GPUs

Anvil User Guide
Gautschi

Purdue's community cluster for faculty and research groups. Powered by AMD EPYC Genoa CPUs and NVIDIA H100 GPUs. Access through the community cluster purchase program.

192 cores/node | 384 GB RAM | H100 80GB GPUs

Gautschi User Guide
Bell

Community Cluster optimized for communities running traditional, tightly-coupled science and engineering applications. Built through a partnership with Dell and AMD, Bell consists of compute nodes with two 64-core AMD EPYC "Rome" processors and 256 GB of memory.

128 cores/node | 256 GB RAM | 100 Gbps HDR Infiniband

Bell User Guide
Negishi

Community Cluster optimized for communities running traditional, tightly-coupled science and engineering applications. Built through a partnership with Dell and AMD, Negishi consists of compute nodes with two 64-core AMD EPYC "Milan" processors and 256 GB of memory.

128 cores/node | 256 GB RAM | 100 Gbps HDR Infiniband

Negishi User Guide
Gilbreth

Community Cluster optimized for communities running GPU intensive applications such as machine learning. Consists of Dell compute nodes with Intel Xeon processors and Nvidia Tesla GPUs.

Gilbreth User Guide
Scholar

A small cluster suitable for classroom learning about high performance computing. Consists of 6 interactive login servers and 16 batch worker nodes, accessible as a typical cluster with a job scheduler or as an interactive resource with a desktop-like environment.

Scholar User Guide

RCAC Resources¶

RCAC Blogs

Dive into insights from RCAC staff covering best practices, new features, and tips for getting the most out of our computing resources.

RCAC Blogs
Workshops & Tutorials

Hands-on training materials from RCAC workshops, covering topics from introductory Linux to advanced parallel computing and GPU programming.

Browse materials
Software Catalog

Browse the complete catalog of software installed across RCAC clusters, including versions, module names, and usage instructions.

Software catalog
Datasets

Access curated research datasets hosted on RCAC systems, including genomics references, machine learning benchmarks, and domain-specific collections.

Dataset catalog

Need Help?¶

Email Support

Reach the RCAC help desk for account issues, software requests, and technical questions.

rcac-help@purdue.edu
Community Discord

Join the Purdue research computing community to chat with peers and staff in real time.

Join Discord
GitHub

Report documentation issues, suggest improvements, or contribute to RCAC open-source projects.

RCAC on GitHub
Contact Details

Find office hours, phone numbers, and other ways to connect with the RCAC support team.

Full contact info