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Modeling and Tolerating Heterogeneous Failures on Large Parallel Systems

Eric Heien 1 Derrick Kondo 1 Ana Gainaru 2 Dan Lapine 2 Bill Kramer 2 Franck Cappello 3, 2 
1 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
3 GRAND-LARGE - Global parallel and distributed computing
LRI - Laboratoire de Recherche en Informatique, LIFL - Laboratoire d'Informatique Fondamentale de Lille, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : As supercomputers and clusters increase in size and complexity, system failures are inevitable. Different hardware components (such as memory, disk, or network) of such systems can have different failure rates. Prior works assume failures equally affect an application, whereas our goal is to provide failure models for applications that reflect their specific component usage. This is challenging because component failure dynamics are heterogeneous in space and time. To this end, we study 5 years of system logs from a production high-performance computing system and model hard ware failures involving processors, memory, storage and net work components. We model each component and construct integrated failure models given the component us age of common supercomputing applications. We show that these application-centric models provide more accurate reliability estimates compared to general models, which improves the efficacy of fault-tolerant algorithms. In particular, we demonstrate how applications can tune their checkpointing strategies to the tailored model.
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Contributor : Arnaud Legrand Connect in order to contact the contributor
Submitted on : Friday, February 15, 2013 - 11:16:24 AM
Last modification on : Friday, November 18, 2022 - 9:23:36 AM


  • HAL Id : hal-00788786, version 1


Eric Heien, Derrick Kondo, Ana Gainaru, Dan Lapine, Bill Kramer, et al.. Modeling and Tolerating Heterogeneous Failures on Large Parallel Systems. IEEE/ACM Supercomputing Conference (SC), 2011, Seatle, United States. pp.1-11. ⟨hal-00788786⟩



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