Tropical cyclones, also known as hurricanes or typhoons, are some of the most powerful weather events on the planet, impacting millions when they make landfall. Predicting them saves lives, but takes an enormous amount of math, including fluid dynamics calculations – exactly the types of simulations GPUs excel at producing. Researchers from the Naval Postgraduate School have proven their model, using GPUs, is five times faster than the CPU model by using U.S. National Science Foundation ACCESS allocations on NCSA’s Delta supercomputer.

Soonpil Kang was a postdoctoral researcher at the Naval Postgraduate School (NPS) working with Frank Giraldo, a distinguished professor in the Department of Applied Mathematics at NPS, when this research was conducted. 

“We focused on how to efficiently simulate a large-scale, realistic tropical cyclone by leveraging GPU capabilities,” said Kang, currently a postdoctoral researcher at Lawrence Livermore National Laboratory (LLNL). 

Kang’s team used a hurricane that had a lot of data they could test their simulation against, collaborating with Dr. Stephen Guimond, an atmospheric scientist at Hampton University, who provided observational data for Hurricane Guillermo, the ninth-most intense Pacific hurricane on record.

“Our results show that using GPUs can save both time and energy compared to pure CPU-based simulations,” Kang said. “We hope that this work can contribute to the acceleration of numerical weather prediction models.”

Research related to this breakthrough has been published in the Journal of Advances in Modeling Earth Systems, and the team has just published a paper about their new GPU-based work in the International Journal of High-Performance Computing Applications.

You can read more detailed information about this research in the original article here. If you’re a researcher in need of more GPUs, visit the ACCESS website.

Resource Provider Institution(s): National Center for Supercomputing Applications (NCSA)
Resources Used: Delta
Affiliations: Naval Postgraduate School
Funding Agency: NSF
Grant or Allocation Number(s): MTH240030

The science story featured here was enabled by the U.S. National Science Foundation’s ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.