S5719 - Hybrid Simulations Using CPU-GPU Paradigm for Reacting Flows
Staff Scientist, United Technologies Research Center
Dr. Jeremiah Lee has 20 years of experience in general area of applied mathematics, computational reactive flow. He has experience in many aspects in this field including, applied mathematics, high performance computing, large scale code development, algorithm development, data reduction, DNS of fundamental flame structures, turbulent flame modeling, acoustics and characteristics in CFD, dynamic chemical kinetics reduction, transition flows, spray dynamics, non-equilibrium multiphase (e.g. superheated fluids) flows, and GPU computing and applications in large scale CFD . His career in applied mathematics started with an investigation of the aerodynamics of baseballs at the Cooper Union. He developed a spectral element based DNS code for reactive flow while he was a graduate student at Princeton. In 1996, he joined the Swiss Federal Institute of Technology (ETH-Z) in Zurich Switzerland where he worked for 6 years as a research staff. In 2002, he returned to the USA and worked at the Combustion Research Facilities at the Sandia National Laboratories. Two years later, he came and joined the combustion group at UTRC. He has 18 publications in refereed journals and has been an invited speaker at the Princeton University, University of Southern California, University of Connecticut, and at the ICDERS.
GPU technology is attractive to computation intensive simulations such as Computational Fluid Dynamics (CFD) of Reacting Flows. A hybrid CPU-GPU paradigm was benchmarked by simulating a canonical CFD problem. A complex turbulent reactive flow was simulated including detailed chemistry that is typically burdensome for CPU based calculations. We achieved 2-5X overall speed-up using CPU-GPU simulations compared to CPU-only simulations. Further details of the CFD problem, hybrid methodology, performance metrics definition and benchmarking results will be presented. This promising technology, if exploited properly, could quickly enable accurate predictions of finite rate chemistry effects, such as pollutant emissions from combustors. ***This talk is part of the "Accelerating Industrial Competitiveness through Extreme-Scale Computing" series chaired by Jack Wells, Director of Science, National Center for Computational Sciences, Oak Ridge National Laboratory.***
Tags: Supercomputing; Computational Fluid Dynamics; Developer - Performance Optimization