Thursday, November 21, 2019 at 11:00am
VanderWerf Hall, 102
27 Graves Place, Holland, MI 49423-3617
“Scalable Algorithms and Hybrid Parallelization Strategies for Multivariate Integration with ParAdapt and CUDA” by Omofolakunmi (Fola) Olagbemi, doctoral candidate at Western Michigan University’s Computer Science Department
The evaluation of numerical integrals finds applications in fields such as High Energy Physics, Bayesian Statistics, Stochastic Geometry, Molecular Modeling and Medical Physics. The erratic behavior of some integrands due to singularities, peaks, or ridges in the integration region suggests the need for reliable algorithms and software that not only provide an estimation of the integral with a level of accuracy acceptable to the user, but also perform this task in a timely manner. We developed ParAdapt, a numerical integration software based on a classic global adaptive strategy, which employs Graphical Processing Units (GPUs) in providing integral evaluations. Specifically, ParAdapt applies adaptive region partitioning strategies developed for efficient integration and mapping to GPUs. The resulting methods render the framework of the classic global adaptive scheme suitable for general functions in moderate dimensions, say 10 to 25. The algorithms presented have been determined to be scalable as evidenced by speedup values in the double and triple digits up to very large numbers of subdivisions. An analysis of the various partitioning and parallelization strategies is given.
Prior to coming to the United States to pursue a PhD degree, Fola worked for some years at PricewaterhouseCoopers in Nigeria, working with teams to provide assurance services to the firm’s clients.
Her research areas include: high performance computing, applying GPUs and CUDA to scientific computations including the estimation of multivariate numerical integrals from diverse fields such as Bayesian Statistics and High Energy Physics.
Fola obtained her Bachelors in Computer Science from University of Lagos, Lagos, Nigeria, and her M.S. degree in Computer Science from the University of Massachusetts at Boston.