Highly scalable linear solvers on thousands of processors. | Zendy

Stefan P. Domino | Zendy; Ian Karlin | Zendy; Christopher Siefert | Zendy; Jonathan Joseph Hu | Zendy; Allen C. Robinson | Zendy; Raymond S. Tuminaro | Zendy

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Highly scalable linear solvers on thousands of processors.

Author(s) -

Stefan P. Domino,

Ian Karlin,

Christopher Siefert,

Jonathan Joseph Hu,

Allen C. Robinson,

Raymond S. Tuminaro

Publication year - 2009

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/993900

Subject(s) - multigrid method , parallel computing , computer science , scalability , kernel (algebra) , synchronization (alternating current) , multi core processor , matrix multiplication , smoothing , domain decomposition methods , sparse matrix , computational science , mathematics , partial differential equation , mathematical analysis , computer network , channel (broadcasting) , physics , combinatorics , database , quantum mechanics , finite element method , gaussian , computer vision , quantum , thermodynamics

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.

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