Intel Parallel Computing Center at UFRC


UF Research Computing has been recently identified as an Intel Parallel Computing Center (IPCC), standing alongside other institutions having researchers leading their fields in modernizing software applications used in high-performance computing. Currently, there are two projects that have been awarded an Intel fellowship for code modernization.


Machine learning

David Ojika

David Ojika is a fourth-year doctoral student of computer engineering, working with Dr. Darin Acosta. Having received his Master’s degree from California State University, David has completed several internships with Intel, working on near-data processors and heterogeneous chip architectures. His project, fully titled “The Potential of the Intel Xeon Phi for Machine Learning in High-energy Physics,” is specifically interested in the large-scale deployment of hardware accelerators, and the acceleration of machine learning for domain-specific workloads in high-energy physics and image understanding.


Climate and weather

Kyueso Park, Jingyin Tang

Kyuseo Park is a second-year doctoral student with the Department of Computer & Information Science Engineering (CISE). He received his Master’s degree from New York State University in 2014, his major focus being high performance databases and spatial databases. At UF, he is working with Dr. Markus Schneider, and is involved in the research on parallel computational geometry. More specifically, his research for this project focuses on the design and implementation of multithreaded geometric algorithms on multi-core processors, intended to improve the query performance of spatial databases and geographical information systems.

Jingyin Tang is a doctoral candidate from the Department of Geography, with a concurrent Master’s degree in computer science from CISE. His research focus is on radar meteorology, tropical meteorology, and high performance computing in spatial modeling and mesoscale weather modeling. Jingyin works with Dr. Corene Matyas in the creation of spatio-temporal models of rainfall patterns of tropical cyclones making landfall in the U.S. by using WSR-88D Doppler radar observations.