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Soft error resilience in Big Data kernels through modular analysis

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Abstract

The shrinking processor feature and operating voltages of processor circuits are making them increasingly vulnerable to soft faults, which calls for fault resilience techniques at both the software and hardware levels under the big data context. To assist software developers in writing fault-resilient big data applications, we propose the tool ErrorSight, which helps them to focus their efforts on code regions and data structures that are most vulnerable to soft errors, understand how numerical errors propagate through the program, and apply fault resilience techniques effectively. ErrorSight achieves this through efficient generation of error profiles leveraging the predictive power of the Boosted Regression Tree model. We use four big data kernels to illustrate the modular analysis mechanism of ErrorSight and show its usefulness in the development of numerical fault-resilience in Big Data.

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Acknowledgments

We are grateful to Vishal Sharma and Arvind Haran, the authors of the original KULFI and for granting us permission to modify it for our experiment purposes. We are also appreciative of the opportunity to be involved in and contribute to KULFI.

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Authors and Affiliations

  1. Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, USA

    Sui Chen & Lu Peng

  2. Lawrence Livermore National Laboratory, Livermore, USA

    Greg Bronevetsky

  3. Department of Experimental Statistics, Louisiana State University, Baton Rouge, USA

    Bin Li

  4. Department of Electrical and Computer Engineering, University of Houston, Houston, USA

    Xin Fu

Authors
  1. Sui Chen
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  2. Greg Bronevetsky
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  3. Lu Peng
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  4. Bin Li
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  5. Xin Fu
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Corresponding author

Correspondence to Lu Peng.

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Chen, S., Bronevetsky, G., Peng, L. et al. Soft error resilience in Big Data kernels through modular analysis. J Supercomput 72, 1570–1596 (2016). https://doi.org/10.1007/s11227-016-1682-2

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  • DOI: https://doi.org/10.1007/s11227-016-1682-2

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