We present a resilient domain-decomposition preconditioner for partial differential equations (PDEs). The algorithm reformulates the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to both soft and hard faults. We discuss an implementation based on a server-client model where all state information is held by the servers, while clients are designed solely as computational units. Servers are assumed to be “sandboxed”, while no assumption is made on the reliability of the clients. We explore the scalability of the algorithm up to ∼12k cores, build an SST/macro skeleton to extrapolate to∼50k cores, and show the resilience under simulated hard and soft faults for a 2D linear Poisson equation.
|Original language||English (US)|
|Title of host publication||High Performance Computing - 31st International Conference, ISC High Performance 2016, Proceedings|
|Editors||Jack Dongarra, Julian M. Kunkel, Pavan Balaji|
|Number of pages||17|
|State||Published - 2016|
|Event||31st International Conference on High Performance Computing, ISC High Performance 2016 - Frankfurt, Germany|
Duration: Jun 19 2016 → Jun 23 2016
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||31st International Conference on High Performance Computing, ISC High Performance 2016|
|Period||06/19/16 → 06/23/16|
Bibliographical noteFunding Information:
Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under Award Numbers 13-016717. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
© Springer International Publishing Switzerland 2016.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)