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.
- Hands on Introduction to Linux:
- Part 3 - Thurs, Jun 23
- Part 4 - Tues, Jun 28
- Introduction to Parallel Computing: Tues, July 5th,
- 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
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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.