SC15 Austin, TX

Caliper: Composite Performance Data Collection in HPC Codes

Authors: David Boehme (Lawrence Livermore National Laboratory), Todd Gamblin (Lawrence Livermore National Laboratory), Peer-Timo Bremer (Lawrence Livermore National Laboratory), Olga T. Pearce (Lawrence Livermore National Laboratory), Martin Schulz (Lawrence Livermore National Laboratory)

Abstract: Correlating performance metrics with program context information is key to understanding HPC application behavior. Given the composite architecture of modern HPC applications, metrics and context information must be correlated from independent places across the software stack. Current data-collection approaches either focus on singular performance aspects, limiting the ability to draw correlations, or are not flexible enough to capture custom, application-specific performance factors. With the Caliper framework, we introduce (1) a flexible data model that can efficiently represent arbitrary performance-related data, and (2) a library that transparently combines performance metrics and program context information provided by source-code annotations and automatic measurement modules. Measurement modules and source-code annotations in different program and system components are independent of each other and can be combined in an arbitrary fashion. This composite approach allows us to easily create powerful measurement solutions that facilitate the correlation of performance data across the software stack.

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