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

Journal Article · Thu May 25 00:00:00 EDT 2017 · Parallel Computing

DOI:https://doi.org/10.1016/j.parco.2017.05.005· OSTI ID:1478742

Rizzi, F. ^[1]; Morris, K. ^[1]; Sargsyan, K. ^[1]; Mycek, P. ^[2]; Safta, C. ^[1]; Le Maître, O. ^[3]; Knio, O. M. ^[4]; Debusschere, B. J. ^[1]

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1. Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
2. Duke Univ., Durham, NC (United States)
3. Centre National de la Recherche Scientifique (CNRS), Orsay (France). Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI)
4. Duke Univ., Durham, NC (United States); King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)

Here, 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.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
• Grant/Contract Number: AC02-05CH11231; AC04-94AL85000
• OSTI ID: 1478742
• Alternate ID(s): OSTI ID: 1550083
• Journal Information: Parallel Computing, Journal Name: Parallel Computing Journal Issue: C Vol. 73; ISSN 0167-8191
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
PDE
Resiliency
domain-decomposition
dynamic voltage/frequency scaling
server-client programming model
silent data corruption