Implicit time integration for seismic wave modeling

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Project Description

Apply and evaluate implicit time integration in seismic wave modeling with modern linear system solvers and preconditioning techniques.​
Program - Applied Mathematics and Computer Science
Division - Computer, Electrical and Mathematical Sciences and Engineering
Center Affiliation - Extreme Computing Research Center
Field of Study - ​Mathematics, Computer Science, Mechanical Engineering or related

About the
Researcher

David E. Keyes

Professor, Applied Mathematics and Computational Science<br/>Director, Extreme Computing Research Center

David E. Keyes
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.

Desired Project Deliverables

​Deliverables: prototyping Matlab code for efficient multigrid solver with complexity analysis Further expectations: C++ code for efficient multigrid solver with integrated cost analysis and performance measurement​