Statistical assertion: a more powerful method for debugging scientific applications

Dinh, Minh Ngoc, Abramson, David and Jin, Chao (2014) Statistical assertion: a more powerful method for debugging scientific applications. Journal of Computational Science, 5 2: 126-134. doi:10.1016/j.jocs.2013.12.002

Author Dinh, Minh Ngoc
Abramson, David
Jin, Chao
Title Statistical assertion: a more powerful method for debugging scientific applications
Journal name Journal of Computational Science   Check publisher's open access policy
ISSN 1877-7503
Publication date 2014-03-01
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.jocs.2013.12.002
Open Access Status Not yet assessed
Volume 5
Issue 2
Start page 126
End page 134
Total pages 9
Place of publication Oxford, United Kingdom
Publisher Elsevier
Language eng
Subject 2614 Theoretical Computer Science
1700 Computer Science
2611 Modelling and Simulation
Abstract Traditional debuggers are of limited value for modern scientific codes that manipulate large complex data structures. Current parallel machines make this even more complicated, because the data structure may be distributed across processors, making it difficult to view/interpret and validate its contents. Therefore, many applications' developers resort to placing validation code directly in the source program. This paper discusses a novel debug-time assertion, called a "Statistical Assertion", that allows using extracted statistics instead of raw data to reason about large data structures, therefore help locating coding defects. In this paper, we present the design and implementation of an 'extendable' statistical-framework which executes the assertion in parallel by exploiting the underlying parallel system. We illustrate the debugging technique with a molecular dynamics simulation. The performance is evaluated on a 20,000 processor Cray XE6 to show that it is useful for real-time debugging.
Keyword Assertion
Parallel architecture
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 2 times in Scopus Article | Citations
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