On-line detection of large-scale parallel application's structure
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
With larger and larger systems being constantly deployed, trace-based performance analysis of parallel applications has become a daunting task. Even if the amount of performance data gathered per single process is small, traces rapidly become unmanageable when merging together the information collected from all processes. In general, an e cient analysis of such a large volume of data is subject to a previous ltering step that directs the analyst's attention towards what is meaningful to understand the observed application behavior. Furthermore, the iterative nature of most scienti c applications usually ends up producing repetitive information. Discarding irrelevant data aims at reducing both the size of traces, and the time required to perform the analysis and deliver results. In this paper, we present an on-line analysis framework that relies on clustering techniques to intelligently select the most relevant information to understand how does the application behave, while keeping the trace volume at a reasonable size.
CitationLlort, G. [et al.]. On-line detection of large-scale parallel application's structure. A: IEEE International Parallel and Distributed Processing Symposium. "IEEE International Symposium on Parallel & Distributed Processing: IPDPS 2010: Atlanta, Georgia, USA: 19-23 April 2010". Atlanta, GA: Institute of Electrical and Electronics Engineers (IEEE), 2010, p. 1-10.
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