The Department of Mathematics and Statistics is pleased to host David E. Keyes from Columbia University for a colloquium talk on Friday, October 26. Keyes is a distinguished researcher in scientific and parallel computing working on large-scale problems from many applications including Department of Energy applications. He is moreover an excellent speaker and the talk should be entertaining for researchers from a wide range of areas. As the Vice President of the Society for Industrial and Applied Mathematics (SIAM), Keyes is particularly interested in technical dialogs with engineers, physicists and computer scientists, and we sincerely invite both faculty and students from all departments.
The talk will be Friday, October 26, 2007, 2:30-3:30 p.m. in LH 3 (Administration Building). A brief reception will follow the talk. The title and abstract, and a bio sketch of the speaker follow. If you would like to meet the speaker, please contact Matthias Gobbert, Mathematics & Statistics, at gobbert@math.umbc.edu.
David E. Keyes Bio
David E. Keyes is the Fu Foundation professor of applied mathematics in the Department of Applied Physics and Applied Mathematics at Columbia University. He has authored or co-authored over 100 publications in computational science and engineering, numerical analysis and computer science, and has delivered over 300 invited presentations at universities, laboratories and industrial research centers. With backgrounds in engineering, applied mathematics and computer science, Keyes works at the algorithmic interface between parallel computing and the numerical analysis of partial differential equations, across a spectrum of
aerodynamic, geophysical and chemically reacting flows. Newton-Krylov-Schwarz parallel implicit methods, introduced in a 1993 paper he co-authored, are now widely used throughout engineering and computational physics, and have been scaled to thousands of parallel
processors. Keyes has received numerous awards for his teaching and research, most recently the Sidney Fernbach Award 2007.
Abstract: Scalable Solver Infrastructure for Computational Science & Engineering
Multiscale, multirate scientific and engineering applications based on systems of partial differential equations possess resolution requirements that demand execution on the highest-capability computers available, which will soon reach the petascale. While the variety of applications is enormous, their needs for mathematical software infrastructure are surprisingly coincident. Implicit methods for transient and equilibrium problems lead after discretization to large, ill-conditioned algebraic systems. The chief to bottleneck to scalability is often the solver. At their current scalability limits, many applications spend a vast majority of their operations in solvers, due to solver algorithmic complexity that is superlinear in the problem size, whereas other phases scale linearly. Furthermore, the solver may be the phase of the simulation with the poorest parallel scalability, due to intrinsic global dependencies. The
Towards Optimal Petascale Simulations (TOPS, www.scidac.gov/math/TOPS.html) project focuses on ameliorating this bottleneck while providing a multilevel programming interface that allows users to advance from initial concerns of correctness and robustness to ultimate concerns of efficiency and performance portability by experimenting with a variety of solvers. We begin with an overview of the diverse petascale hardware roadmaps at the laboratories served by the TOPS project, with such applications as electromagnetism, magnetohydrodynamics, and quantum chromodynamics. We then describe the algorithmic and software roadmap of TOPS, which includes such well-known packages as Hypre, PETSc,
SUNDIALS, SuperLU, and Trilinos.
