Skip to content

New contentThe CHPC has a new page summarizing machine learning and artifical intelligence resources.

Center for High Performance Computing

Research Computing and Data Support for the University Community

 

In addition to deploying and operating high-performance computational resources and providing advanced user support and training, CHPC serves as an expert team to broadly support the increasingly diverse research computing and data needs on campus. These needs include support for big data, big data movement, data analytics, security, virtual machines, Windows science application servers, protected environments for data mining and analysis of protected health information, advanced networking, and more.

If you are new to the CHPC, the best place to learn about CHPC resources and policies is our Getting Started page.

Have a question? Please check our Frequently Asked Questions page and contact us if you require assistance or have further questions or concerns.

Announcing the Upcoming Retirements of Julia Harrison and Anita M. Orendt
Julia Harrison
Julia Harrison

After nearly four decades of dedicated service at the University of Utah, Julia Harrison is retiring as the Operations Director of the Center for High Performance Computing.

Read more
Anita M. Orendt
Anita M. Orendt

Anita M. Orendt is a dedicated educator and researcher with a rich background in physical chemistry. Anita has made significant contributions to the academic community at the University of Utah.

Read more
Upcoming Events:

CHPC PE DOWNTIME: Partial Protected Environment Downtime  -- Oct 24-25, 2023

Posted October 18th, 2023


CHPC INFORMATION: MATLAB and Ansys updates

Posted September 22, 2023


CHPC SECURITY REMINDER

Posted September 8th, 2023

CHPC is reaching out to remind our users of their responsibility to understand what the software being used is doing, especially software that you download, install, or compile yourself. Read More...

News History...

hidden outbreak of the bacterial infection following Hurricane Maria

Early Biothreat Detection with Unsupervised Machine Learning

By Julia Lewis, Kelly Peterson, Wathsala Widanagamaachchi, Clifton Baker, Fangxiang Jiao, Chris Nielson, Makoto Jones (PI)

Department of Internal Medicine, University of Utah

We leverage neural network autoencoder models to rapidly detect biological events such as those following natural disasters. After training these models, they learn what is common in Emergency Department (ED) visits in Veterans Affairs medical centers. When the reconstruction distance is high they indicate that the visit is not common even if we do not know the diagnosis.  

System Status

General Environment

last update: 2024-11-06 16:41:03
General Nodes
system cores % util.
kingspeak 935/972 96.19%
notchpeak 3101/3212 96.54%
lonepeak 1506/1932 77.95%
Owner/Restricted Nodes
system cores % util.
ash Status Unavailable
notchpeak 15619/22068 70.78%
kingspeak 2816/5244 53.7%
lonepeak 36/416 8.65%

Protected Environment

last update: 2024-11-06 16:40:04
General Nodes
system cores % util.
redwood 258/628 41.08%
Owner/Restricted Nodes
system cores % util.
redwood 1064/6472 16.44%


Cluster Utilization

Last Updated: 11/4/24