On using incremental profiling for the performance analysis of shared memory parallel applications

Authors:
Karl Fuerlinger;Michael Gerndt;Jack Dongarra
Affiliations:
Innovative Computing Laboratory, Department of Computer Science, University of Tennessee;Lehrstuhl für Rechnertechnik und Rechnerorganisation, Institut für Informatik, Technische Universität München;Innovative Computing Laboratory, Department of Computer Science, University of Tennessee
Venue:
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
Year:
2007

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Abstract

Profiling is often the method of choice for performance analysis of parallel applications due to its low overhead and easily comprehensible results. However, a disadvantage of profiling is the loss of temporal information that makes it impossible to causally relate performance phenomena to events that happened prior or later during execution. We investigate techniques to add temporal dimension to profiling data by incrementally capturing profiles during the runtime of the application and discuss the insights that can be gained from this type of performance data. The context in which we explore these ideas is an existing profiling tool for OpenMP applications.