Space-efficient time-series call-path profiling of parallel applications

Authors:
Zoltán Szebenyi;Felix Wolf;Brian J. N. Wylie
Affiliations:
Jülich Supercomputing Centre, Jülich, Germany and RWTH Aachen University, Aachen, Germany;Jülich Supercomputing Centre, Jülich, Germany and RWTH Aachen University, Aachen, Germany;Jülich Supercomputing Centre, Jülich, Germany
Venue:
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Year:
2009

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Abstract

The performance behavior of parallel simulations often changes considerably as the simulation progresses --- with potentially process-dependent variations of temporal patterns. While call-path profiling is an established method of linking a performance problem to the context in which it occurs, call paths reveal only little information about the temporal evolution of performance phenomena. However, generating call-path profiles separately for thousands of iterations may exceed available buffer space --- especially when the call tree is large and more than one metric is collected. In this paper, we present a runtime approach for the semantic compression of call-path profiles based on incremental clustering of a series of single-iteration profiles that scales in terms of the number of iterations without sacrificing important performance details. Our approach offers low runtime overhead by using only a condensed version of the profile data when calculating distances and accounts for process-dependent variations by making all clustering decisions locally.