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Author Lee, Sunggu ♦ Shin, Kang Geun
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©1994
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Centralized and distributed self-diagnosis ♦ Comparison testing ♦ Fault-tolerant computing ♦ Probabilistic diagnosis ♦ System-level diagnosis ♦ System-level testing
Abstract This paper critically surveys methods for the automated probabilistic diagnosis of large multiprocessor systems. In recent years, much of the work on system-level diagnosis has focused on probabilistic methods, which can diagnose intermittently faulty processing nodes and can be applied in $\textit{general}$ situations on $\textit{general}$ interconnection networks. The theory behind the probabilistic diagnosis methods is explained, and the various diagnosis algorithms are described in simple terms with the aid of a running example. The diagnosis methods are compared and analyzed, and a chart is produced, showing the comparative advantages of the various diagnosis algorithms on the basis of several factors important to the probabilistic diagnosis.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1994-03-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 26
Issue Number 1
Page Count 19
Starting Page 121
Ending Page 139

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Source: ACM Digital Library