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Rosen Center For Advanced Computing

RCAC provides access to leading-edge computational and data storage systems as well as expertise in a broad range of high-performance computing activities.

🎉 Welcome to the new RCAC Documentation Website! Discover what's new →

Welcome to RCAC Documentation

Important: Use Microsoft Authenticator to Access Purdue RCAC Resources Starting May 11, 2026

Beginning May 11, 2026, RCAC will move from Duo Mobile to Microsoft Authenticator for logging in to Purdue’s research computing and supercomputing systems (excluding Anvil).

To ensure uninterrupted access, please complete the steps below before May 11. What you need to do:

  1. Enroll in Microsoft Multi-Factor Authentication (MFA) using the Microsoft Authenticator app (if you have not already).
  2. Set your default sign-in method in Microsoft MFA to your preferred method.

After May 11, Duo Mobile will no longer work for accessing RCAC systems (excluding Anvil).

Please contact rcac-help@purdue.edu If you have any questions or need help.

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

  • Install Software


    Find pre-installed modules via the LMOD system or request software from the RCAC help desk.

    Software installation guide

  • RCAC Website


    Need to learn more about RCAC? Visit our official website.

    RCAC Website

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 | 256GB-1TB RAM | A100/H100 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 | 384GB-1.5TB RAM | H100 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


    A community Discord for Purdue researchers, RCAC staff, and other organizations to discuss research computing 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