CHPC - Research Computing and Data Support for the University
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 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, and advanced networking. Visit our Getting Started page for more information.
CHPC ANNOUNCEMENT: Summer Presentations Schedule
Upcoming Presentations:
- Hands on Introduction to Linux:
1-3pm- Part 3 - Thurs, Jun 23
- Part 4 - Tues, Jun 28
- Introduction to Parallel Computing: Tues, July 5th,
1-3pm - Hands on Introduction to Python: 1-3PM
- Part 1 - Thurs, Jul 7
- Part 2 - Tues, Jul 12
- Part 3 - Thurs, Jul 14
- Part 4 (Numpy part 1) - Tues, Jul 19
- Part 5 (Numpy part 2) - Thurs, Jul 21
CHPC DOWNTIME: General Environment Clusters - May 25&26, 2022 - Reminder and Additional Impact
Posted April 21, 2022
Updated May 11, 2022
CHPC ANNOUNCEMNT: NEW General Environment scratch
Posted May 20, 2022
CHPC ANNOUNCEMENT: Reminder that University of Utah Affiliate Accounts Expiration
Posted May 9, 2022
Allocation Requests for Summer 2022 are due June 1, 2022
Posted May 2, 2022
CHPC OUTAGE: May 1, 2022 starting at about 5pm
Posted May 2, 2022
CHPC ANNOUNCEMENT: Summer Presentation Schedule Available!
Posted April 15th, 2022
Posted March 21st, 2022
DOWNTIME: Select CHPC systems, Tuesday, March 22, 2022 starting at 7am
Posted March 8th, 2022
CHPC ANNOUNCEMENT: Changes in Google Drive
Posted February 14th, 2022
Fall 2021 CHPC Newsletter
CHPC ANNOUNCEMENT: CHPC staff working both remotely and hybrid schedules.
News History...
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
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| ash | Status Unavailable | |
| notchpeak | Status Unavailable | |
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Protected Environment
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| redwood | Status Unavailable | |
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| redwood | Status Unavailable | |