Close the Convergence-Parallelism Gap
Yin Yang
Abstract
While physics simulation is becoming one of the mainstream drivers for graphics, vision, physical intelligence, and embodied AI, a key challenge is maintaining reasonable computational efficiency with parallel hardware. For strongly coupled systems (like solid FEM), parallel algorithms do not perform well as the complexity of the problem scales up. We show a set of new algorithms that allow us to pursue maximized parallelism and nearly second-order convergence at the same time.The key observation is: parallel simulation is a problem of integration.
Bio
Dr. Yin Yang is currently an Associate Professor with the Kahlert School of Computing at the University of Utah. Before joining the U, he was a faculty member at Clemson University and University of New Mexico. He received Ph.D. degree of Computer Science from The University of Texas, Dallas in 2013 (the awardee of David Daniel Fellowship Prize). He was a Research/Teaching Assistant at UT Dallas as well as UT Southwestern Medical Center. His research mainly focuses on real-time physics-based computer graphics, animation and simulation with a strong emphasis on interdisciplinarity. He was a Research Intern in Microsoft Research Asia in 2012. He received NSF CRII (2015) and CAREER (2019) awards.
