Speaker: David Keyes, KAUST

Title: Efficient Computation through Tuned Approximation

Abstract: Numerical software is being reinvented to provide opportunities to tune dynamically the accuracy of computation to the requirements of the application, resulting in savings of memory, time, and energy. Floating point computation in science and engineering has a history of “oversolving” relative to expectations for many models. So often are real datatypes defaulted to double precision that GPUs did not gain wide acceptance until they provided in hardware operations not required in their original domain of graphics. However, computational science is now reverting to employ lower precision arithmetic where possible. Many matrix operations considered at a blockwise level allow for lower precision and many blocks can be approximated with low rank near equivalents. This leads to smaller memory footprint, which implies higher residency on memory hierarchies, leading in turn to less time and energy spent on data copying, which may even dwarf the savings from fewer and cheaper flops. We provide examples from several application domains, including a look at campaigns in geospatial statistics, seismic processing, genome wide association studies, and climate emulation that earned Gordon Bell Prize finalist status in 2022, 2023, and twice in 2024.

Bio: David Keyes is Professor of Applied Mathematics and Computational Science and the Director of the Extreme Computing Research Center at the King Abdullah University of Science and Technology (KAUST), having served as the founding Dean of the Division of Mathematical and Computer Sciences and Engineering from 2009 to 2012. He joined the Office of President Tony Chan in October 2018 as Senior Associate, with responsibilities for strategic priorities and institutional partnerships. At KAUST, he is also an affiliate faculty member in Computer Science and Mechanical Engineering. Keyes retains an Adjunct Professorship as the former Fu Foundation Chair Professor in Applied Physics and Applied Mathematics at Columbia University and is a faculty affiliate of several laboratories of the U.S. Department of Energy. He graduated summa cum laude in Aerospace and Mechanical Sciences with a certificate in Engineering Physics from Princeton in 1978 and earned a doctorate in Applied Mathematics from Harvard in 1984. Keyes was awarded an NSF Presidential Young Investigator Award as an Assistant Professor of Mechanical Engineering at Yale University in 1989. For his algorithmic influence in scientific simulation, Keyes has been recognized as a Fellow of the Society for Industrial and Applied Mathematics (SIAM), a Fellow of the American Mathematical Society (AMS), and a Fellow of the American Association for the Advancement of Science (AAAS). He shared the Gordon Bell Prize of the ACM in 1999. He received the Sidney Fernbach Award of the IEEE Computer Society in 2007. Author or editor of more than a dozen U.S. federal agency reports and member of several federal advisory committees on computational science and engineering and high performance computing, in 2011, Keyes received the SIAM Prize for Distinguished Service to the Profession.

This talk will be offered in a hybrid format. If you wish to participate remotely, please send an email to cr2090@columbia.edu.