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 Retirement of Anita M. Orendt and Upcoming Retirement of Julia Harrison
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...

Graph processing time vs input size

Optimization of Supercomputing Techniques to Compute Opto-electronic Energetics of Catalysts

By Alex Beeston, Caleb Thomson, Ricardo Romo, D. Keith Roper

Department of Biological Engineering, Utah State University

Electromagnetic spectra of catalytic particles can be compared using the Discrete Dipole Approximation (DDA) to simulate the optoelectronic energies of noble metal catalysts. However, DDA requires heavy computational power to generate results in reasonable amounts of time. In this study, simulations of the opto-electronic energies of nano-scale spheres catalysts represented by sets of platinum dipoles in varying levels of resolution are performed using DDA to examine the effect of input size on run time.

DDA was performed in this study by downloading and compiling source code, generating target and parameter files, submitting jobs via SLURM scheduling, and visualizing results. Fast running times of DDA enables more opportunity to examine the opto-electronic behavior of more catalysts, and rational design and fabrication of optimally distributed catalyst particles could eventually transform the activity and economics of chemical and biochemical reactions.

Running the samples in parallel produced minor decreases in running time for only the samples with an input size of at least 65,267 dipole points. For sample sizes less than or equal to 33,401, the running time either increased slightly or did not change by wing parallel processing.

System Status

General Environment

last update: 2025-01-17 16:21:02
General Nodes
system cores % util.
kingspeak 833/952 87.5%
notchpeak 2545/3212 79.23%
lonepeak 1585/1596 99.31%
Owner/Restricted Nodes
system cores % util.
ash Status Unavailable
notchpeak 14288/21996 64.96%
kingspeak 3550/5216 68.06%
lonepeak 416/416 100%

Protected Environment

last update: 2025-01-17 16:20:05
General Nodes
system cores % util.
redwood 185/616 30.03%
Owner/Restricted Nodes
system cores % util.
redwood 1690/6540 25.84%


Cluster Utilization

Last Updated: 1/15/25