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Predicting SDC Vulnerability of Instructions Based on Random Forests Algorithm

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Algorithms and Architectures for Parallel Processing (ICA3PP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11336))

Abstract

Silent Data Corruptions (SDCs) is a serious reliability issue in many domains of computer system. Selectively protecting of the program instructions that have a higher SDC vulnerability is one of the research hot spots in computer reliability field at present. A number of algorithms have already been presented to tackle this problem. However, many of them require tens of thousands of fault injection experiments, which are highly time and resource intensive. This paper proposes SDCPredictor, a novel solution that identify the SDC-vulnerable instructions based on random forests algorithm. SDCPredictor are based on static and dynamic features of the program alone, and do not require fault injections to be performed. SDCPredictor selectively protects the most SDC-vulnerable instructions in the program subject to a given performance overhead bound. Our experimental results show that SDCPredictor can obtain higher SDC detection efficiency than previous similar techniques.

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Acknowledgment

This research was supported by the National Natural Science Foundation of China under grant No. 61370134, the National High Technology Research and Development Program of China (863 Program) under grant No. 2013AA013901.

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Authors and Affiliations

  1. Computer Department, Beijing Institute of Technology, Beijing, China

    LiPing Liu, LinLin Ci & Wei Liu

Authors
  1. LiPing Liu
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  2. LinLin Ci
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  3. Wei Liu
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Corresponding author

Correspondence to LiPing Liu .

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Editors and Affiliations

  1. Rutgers University–Newark, Newark, NJ, USA

    Jaideep Vaidya

  2. Guangzhou University, Guangzhou, China

    Jin Li

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Liu, L., Ci, L., Liu, W. (2018). Predicting SDC Vulnerability of Instructions Based on Random Forests Algorithm. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_44

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  • DOI: https://doi.org/10.1007/978-3-030-05057-3_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05056-6

  • Online ISBN: 978-3-030-05057-3

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