DavidKeyes_Formal_2014-224x300MICDE Seminar: David Keyes

David Keyes directs the Extreme Computing Research Center at the King Abdullah University of Science and Technology (KAUST). He earned a BSE in Aerospace and Mechanical Sciences from Princeton in 1978 and PhD in Applied Mathematics from Harvard in 1984. Keyes works at the interface between parallel computing and the numerical analysis of PDEs, with a focus on scalable implicit solvers. Newton-Krylov-Schwarz (NKS), Additive Schwarz Preconditioned Inexact Newton (ASPIN), and Algebraic Fast Multipole (AFM) methods are methods he helped name and is helping to popularize.  Before joining KAUST as a founding dean in 2009, he led scalable solver software projects in the ASCI and SciDAC programs of the US DOE, headed university collaboration programs at NASA’s ICASE and the LLNL’s ISCR, and taught at Columbia, Old Dominion, and Yale Universities.  He holds a Gordon Bell Prize and a Sidney Fernbach Award, is a Fellow of AMS and SIAM, and was the recipient of the 2011 SIAM Prize for Distinguished Service to the Profession. 

Algorithmic Adaptations to Extreme Scale

1 – 2:30 p.m., Thurs., March 19
1010 Herbert H. Dow Building

Algorithmic adaptations to use next-generation computers closer to their potential are underway throughout scientific computing. Instead of squeezing out flops – the traditional goal of algorithmic optimality, which served as a reasonable proxy for all associated costs – algorithms must now squeeze synchronizations, memory, and data transfers, while extra flops on locally cacheable data typically represent only small costs in time and energy. Today’s scalable solvers, in particular, exploit frequent global synchronizations. After decades of programming model stability with bulk synchronous processing (BSP), new programming models and new algorithmic capabilities (to make forays into, e.g., data assimilation, inverse problems, and uncertainty quantification) must be co-designed with the hardware. This talk will briefly recap the architectural constraints, mention some related work at KAUST, and outline future directions.