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Improving fault resilience of high performance applications

Published: 11 November 2006 Publication History

Abstract

For large-scale systems with hundreds to thousands of nodes, failures are likely to be more frequent as the system reliability decreases exponentially with the increasing count of components. Many parallel applications that span a large number of nodes are designed to run for days or weeks until completion. Hence, application-level fault resilience is of critical importance to the continued scaling of high performance computing (HPC). In this poster, we present and evaluate an adaptive fault resilience framework for HPC applications which adaptively selects an optimal corrective or preventive action based upon failure predictions at runtime. The proposed framework is implemented with a production-level MPI package and assessed with a variety of real-world parallel applications on production HPC systems. The experiment results demonstrate promising performance improvement of FT-Pro against traditional checkpointing/recovery schemes under a wide range of prediction accuracies and application characteristics.

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cover image ACM Conferences
SC '06: Proceedings of the 2006 ACM/IEEE conference on Supercomputing
November 2006
746 pages
ISBN:0769527000
DOI:10.1145/1188455
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 11 November 2006

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SC '06 Paper Acceptance Rate 54 of 239 submissions, 23%;
Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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