Exploring the interplay of resilience and energy consumption for a task-based partial differential equations preconditioner

F. Rizzi, K. Morris, K. Sargsyan, P. Mycek, C. Safta, O. Le Maître, Omar Knio, B.J. Debusschere

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We discuss algorithm-based resilience to silent data corruptions (SDCs) in a task-based domain-decomposition preconditioner for partial differential equations (PDEs). The algorithm exploits a reformulation of the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to SDCs. The implementation is based on a server-client model where all state information is held by the servers, while clients are designed solely as computational units. Scalability tests run up to ∼ 51K cores show a parallel efficiency greater than 90%. We use a 2D elliptic PDE and a fault model based on random single and double bit-flip to demonstrate the resilience of the application to synthetically injected SDC. We discuss two fault scenarios: one based on the corruption of all data of a target task, and the other involving the corruption of a single data point. We show that for our application, given the test problem considered, a four-fold increase in the number of faults only yields a 2% change in the overhead to overcome their presence, from 7% to 9%. We then discuss potential savings in energy consumption via dynamic voltage/frequency scaling, and its interplay with fault-rates, and application overhead.
Original languageEnglish (US)
Pages (from-to)16-27
Number of pages12
JournalParallel Computing
Volume73
DOIs
StatePublished - May 25 2017

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