Highly scalable linear solvers on thousands of processors.

Domino, Stefan Paul (Sandia National Laboratories, Albuquerque, NM) ; Karlin, Ian (University of Colorado at Boulder, Boulder, CO) ; Siefert, Christopher (Sandia National Laboratories, Albuquerque, NM) ; Hu, Jonathan Joseph ; Robinson, Allen Conrad (Sandia National Laboratories, Albuquerque, NM) ; Tuminaro, Raymond Stephen

Sandia National Laboratories

关键词: Synchronization;    Parallel Processing;    Algorithms;    97 Mathematical Methods And Computing;    A Codes;   

DOI  :  10.2172/993900
RP-ID  :  SAND2009-6197
RP-ID  :  AC04-94AL85000
RP-ID  :  993900

美国|英语

来源: UNT Digital Library

【 摘 要 】

In this report we summarize research into new parallel algebraic multigrid (AMG) methods. We first provide a introduction to parallel AMG. We then discuss our research in parallel AMG algorithms for very large scale platforms. We detail significant improvements in the AMG setup phase to a matrix-matrix multiplication kernel. We present a smoothed aggregation AMG algorithm with fewer communication synchronization points, and discuss its links to domain decomposition methods. Finally, we discuss a multigrid smoothing technique that utilizes two message passing layers for use on multicore processors.