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A Bayesian Reliability Analysis of Neutron-Induced Errors in High Performance Computing Hardware

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  • Curtis B. Storlie
  • Sarah E. Michalak
  • Heather M. Quinn
  • Andrew J. Dubois
  • Steven A. Wender
  • David H. Dubois

Abstract

A soft error is an undesired change in an electronic device's state, for example, a bit flip in computer memory, that does not permanently affect its functionality. In microprocessor systems, neutron-induced soft errors can cause crashes and silent data corruption (SDC). SDC occurs when a soft error produces a computational result that is incorrect, without the system issuing a warning or error message. Hence, neutron-induced soft errors are a major concern for high performance computing platforms that perform scientific computation. Through accelerated neutron beam testing of hardware in its field configuration, the frequencies of failures (crashes) and of SDCs in hardware from the Roadrunner platform, the first Petaflop supercomputer, are estimated. The impact of key factors on field performance is investigated and estimates of field reliability are provided. Finally, a novel statistical approach for the analysis of interval-censored survival data with mixed effects and uncertainty in the interval endpoints, key features of the experimental data, is presented. Supplementary materials for this article are available online.

Suggested Citation

  • Curtis B. Storlie & Sarah E. Michalak & Heather M. Quinn & Andrew J. Dubois & Steven A. Wender & David H. Dubois, 2013. "A Bayesian Reliability Analysis of Neutron-Induced Errors in High Performance Computing Hardware," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 429-440, June.
    • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:502:p:429-440
    DOI: 10.1080/01621459.2013.770694
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    Cited by:

    1. Curtis B. Storlie & Brian J. Reich & William N. Rust & Lawrence O. Ticknor & Amanda M. Bonnie & Andrew J. Montoya & Sarah E. Michalak, 2017. "Spatiotemporal Modeling of Node Temperatures in Supercomputers," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 92-108, January.

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