Event Flow Graphs for MPI Performance Monitoring and Analysis

Xavier Aguilar (), Karl Fürlinger and Erwin Laure
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Xavier Aguilar: KTH Royal Institute of Technology, High Performance Computing and Visualization Department (HPCViz), and Swedish E-Science Research Center (SeRC)
Karl Fürlinger: Ludwig-Maximilians-Universität (LMU) Munich, Computer Science Department, MNM Team
Erwin Laure: KTH Royal Institute of Technology, High Performance Computing and Visualization Department (HPCViz), and Swedish E-Science Research Center (SeRC)

Chapter Chapter 8 in Tools for High Performance Computing 2015, 2016, pp 103-115 from Springer

Abstract: Abstract Classical performance analysis methodologies use either execution traces with fine-grained data or profiles with aggregates. Event traces provide the finest level of detail on application behavior, however, they are infeasible at extreme scales due to the huge amount of information they contain. In contrast, profiles are much more scalable but they lack the temporal order between events. In this paper, we present the use of event flow graphs for performance characterization of MPI applications. Event flow graphs capture statistics on the events performed by the application while preserving the temporal order of such events. Thus, they stand in between tracing and profiling, and are a good complement for classical performance analysis approaches such as event traces or profiles. Furthermore, event flow graphs can be used for means other than visual exploration of performance data. For example, graphs can be utilized as compressed representations of event traces, or to automatically detect the program structure of iterative applications at runtime without any source code analysis.

Keywords: Event Flow Graph (EFGs); Visual Performance Analysis; Trace Compression; Main Simulation Loop; Nested Loop Structure (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-39589-0_8

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