Zoom: https://go.ncsa.illinois.edu/DavidKeyesTalk
Pizza, cookies, & drinks provided for those who attend in-person
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 2024.
Bio:
David Keyes is a professor of Applied Mathematics, Computer Science, and Mechanical Engineering at the King Abdullah University of Science and Technology (KAUST), where he was a founding Dean in 2009, directed the Extreme Computing Research Center, and currently serves in the Office of the President. He is also an adjunct professor of Applied Mathematics and Applied Physics at Columbia University, where he formerly held the Fu Foundation Chair. He works at the interfaces of parallel computing with PDEs and statistics, with a focus on scalable algorithms that exploit data sparsity. He is known for the development of scalable nonlinearly implicit Newton-Krylov-Schwarz algebraic solvers and the nonlinear preconditioning methods ASPIN and NEPIN, which have recently migrated from PDEs to NNs. Before joining KAUST, Keyes led multi-institutional scalable solver software projects in the SciDAC and ASCI programs of the US Department of Energy (DoE), ran university collaboration programs at US DoE and NASA institutes, and taught at Columbia, Old Dominion, and Yale Universities. He is a Fellow of SIAM, the AMS, and the AAAS. He has been awarded the Gordon Bell Prize from the ACM, the Sidney Fernbach Award from the IEEE Computer Society, and the SIAM Prize for Distinguished Service to the Profession. He earned a B.S.E. in Aerospace and Mechanical Sciences from Princeton in 1978 and a Ph.D. in Applied Mathematics from Harvard in 1984.
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