Published in

Elsevier, Journal of Computational Science, 2(5), p. 126-134

DOI: 10.1016/j.jocs.2013.12.002

Links

Tools

Export citation

Search in Google Scholar

Statistical assertion: a more powerful method for debugging scientific applications

Journal article published in 2014 by Minh Ngoc Dinh, David Abramson ORCID, Chao Jin
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Red circle
Postprint: archiving forbidden
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

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.