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

• 成果类型: Article
• 署名作者: Storlie, Curtis B.; Michalak, Sarah E.; Quinn, Heather M.; DuBois, Andrew J.; Wender, Steven A.; DuBois, David H.
• 署名单位: United States Department of Energy (DOE); Los Alamos National Laboratory; United States Department of Energy (DOE); Los Alamos National Laboratory
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.770694
• 发表日期: 2013
• 页码: 429-440
• 关键词: PROPORTIONAL HAZARDS MODEL; interval-censored data; variable selection; regression-analysis; soft errors; Lasso

摘要

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.