Scalability of partial differential equations preconditioner resilient to soft and hard faults

Published

Conference Paper

© Springer International Publishing Switzerland 2016. 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.

Full Text

Duke Authors

Cited Authors

  • Morris, K; Rizzi, F; Sargsyan, K; Dahlgren, K; Mycek, P; Safta, C; Le Maître, O; Knio, O; Debusschere, B

Published Date

  • January 1, 2016

Published In

Volume / Issue

  • 9697 /

Start / End Page

  • 469 - 485

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

International Standard Book Number 13 (ISBN-13)

  • 9783319413204

Digital Object Identifier (DOI)

  • 10.1007/978-3-319-41321-1_24

Citation Source

  • Scopus