TALK TITLE: High Performance Computational Bioinformatics
"Accelerating innovation to meet biological challenges:' the theme of InCoB 2022,includes accelerating the computational tools of biologists and bioinformaticians. Architectural and algorithmic developments have each contributed to dizzying advances in biocomputing applications over the past several decades. The architectural advances from Moore's Law, Denard scaling, concurrency, and heterogeneous accelerators are in many respects "built in" to the biocomputing infrastructure, while many algorithmic advances await adoption.
We take inventory of various scalable hierarchical algorithms, including a few developed and implemented at KAUST, with demonstrated or potential application to biocomputing tasks such as ridge regression in GWAS, force computations in MD, machine learning, and data compression, with the overall goal of enriching direct collaboration between the high performance computing and computational biology communities.
David Keyes is Professor of Applied Mathematics and Computational Science and the Director of the Extreme Computing Research Center, having served as the Dean of the Division of Mathematical and Computer Sciences and Engineering at KAUST for its first 3.5 years. Also an Adjunct Professor and former Fu Foundation Chair Professor in Applied Physics and Applied Mathematics at Columbia University, and an affiliate of several laboratories of the U.S. Department of Energy, Keyes graduated in Aerospace and Mechanical Sciences from Princeton in 1978 and earned a doctorate in Applied Mathematics from Harvard in 1984. Before joining KAUST among the founding faculty, he led scalable solver software projects in the ASCI and SciDAC programs of the U.S. Department of Energy.
Keyes works at the algorithmic interface between parallel computing and the numerical analysis of partial differential equations, with a focus on implicit scalable solvers for emerging architectures and their use in the many large-scale applications in energy and environment governed by conservation laws that demand high performance because of high resolution, high dimension, high fidelity physical models, or the "multi-solve" requirements of optimization, control, sensitivity analysis, inverse problems, data assimilation, or uncertainty quantification.
He has named and contributed to Newton-Krylov-Schwarz (NKS), Additive Schwarz Preconditioned Inexact Newton (ASPIN), and Algebraic Fast Multipole (AFM) methods for large sparse linear and nonlinear systems arising from PDEs. Through the ECRC, he now works on meeting the requirements of drastic reductions in communication and synchronization, increases in concurrency for cores sharing memory locally, local load redistribution, and algorithm-based fault tolerance for these and other algorithms.