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Reliability-aware performance model for optimal GPU-enabled cluster environment

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Abstract

Given that the reliability of a very large-scaled system is inversely related to the number of computing elements, fault tolerance has become a major concern in high performance computing including the most recent deployments with graphic processing units (GPUs). Many fault tolerance strategies, such as the checkpoint/restart mechanism, have been studied to mitigate failures within such systems. However, fault tolerance mechanisms generate additional costs and these may cause a significant performance drop if it is not used carefully. This paper presents a novel fault tolerance scheduling model that explores the interplay between the GPGPU application performance and the reliability of a large GPU system. This work focuses on the checkpoint scheduling model that aims to minimize fault tolerance costs. Additionally, a GPU performance analysis is conducted. Furthermore, the effect of a checkpoint/restart mechanism on the application performance is thoroughly studied and discussed.

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Acknowledgments

This work was partially supported by the grants CNS-0834483, EPS-1003897 and TE97/2010.

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Authors and Affiliations

  1. Department of Computer Science, Louisiana Tech University, Ruston, Louisiana, USA

    Supada Laosooksathit & Chokchai Leangsuksun

  2. Department of Mathematics and Statistics, Louisiana Tech University, Ruston, Louisiana, USA

    Raja Nassar & Mihaela Paun

  3. National Institute for Research and Development for Biological Sciences, Bucharest, Romania

    Mihaela Paun

Authors
  1. Supada Laosooksathit
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  2. Raja Nassar
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  3. Chokchai Leangsuksun
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  4. Mihaela Paun
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Corresponding author

Correspondence to Mihaela Paun.

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Laosooksathit, S., Nassar, R., Leangsuksun, C. et al. Reliability-aware performance model for optimal GPU-enabled cluster environment. J Supercomput 68, 1630–1651 (2014). https://doi.org/10.1007/s11227-014-1128-7

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  • DOI: https://doi.org/10.1007/s11227-014-1128-7

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