EconPapers    
Economics at your fingertips  
 

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

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

Journal of the American Statistical Association, 2013, vol. 108, issue 502, 429-440

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.

Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2013.770694 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:108:y:2013:i:502:p:429-440

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2013.770694

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:jnlasa:v:108:y:2013:i:502:p:429-440