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Automatic On-Line Detection of MPI Application Structure with Event Flow Graphs
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).ORCID iD: 0000-0001-9693-6265
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).ORCID iD: 0000-0002-9901-9857
2015 (English)In: EURO-PAR 2015: PARALLEL PROCESSING, Springer Berlin/Heidelberg, 2015, 70-81 p.Conference paper (Refereed)
Abstract [en]

The deployment of larger and larger HPC systems challenges the scalability of both applications and analysis tools. Performance analysis toolsets provide users with means to spot bottlenecks in their applications by either collecting aggregated statistics or generating loss-less time-stamped traces. While obtaining detailed trace information is the best method to examine the behavior of an application in detail, it is infeasible at extreme scales due to the huge volume of data generated. In this context, knowing the application structure, and particularly the nesting of loops in iterative applications is of great importance as it allows, among other things, to reduce the amount of data collected by focusing on important sections of the code. In this paper we demonstrate how the loop nesting structure of an MPI application can be extracted on-line from its event flow graph without the need of any explicit source code instrumentation. We show how this knowledge on the application structure can be used to compute postmortem statistics as well as to reduce the amount of redundant data collected. To that end, we present a usage scenario where this structure information is utilized on-line (while the application runs) to intelligently collect fine-grained data for only a few iterations of an application, considerably reducing the amount of data gathered.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015. 70-81 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 9233
Keyword [en]
Application structure detection, Flow graph analysis, Performance monitoring, Online analysis, Automatic loop detection
National Category
Computer Science
URN: urn:nbn:se:kth:diva-177430DOI: 10.1007/978-3-662-48096-0_6ISI: 000363786800006ScopusID: 2-s2.0-84944047051ISBN: 978-3-662-48096-0; 978-3-662-48095-3ISBN: 978-3-662-48095-3OAI: diva2:873375
21st International Conference on Parallel and Distributed Computing (Euro-Par), AUG 24-28, 2015, Vienna, AUSTRIA

QC 20151124

Available from: 2015-11-24 Created: 2015-11-20 Last updated: 2015-11-24Bibliographically approved

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