A Flexible Data Model to Support Multi-domain Performance Analysis

Martin Schulz (), Abhinav Bhatele, David Böhme, Peer-Timo Bremer, Todd Gamblin, Alfredo Gimenez and Kate Isaacs
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Martin Schulz: Lawrence Livermore National Laboratory
Abhinav Bhatele: Lawrence Livermore National Laboratory
David Böhme: Lawrence Livermore National Laboratory
Peer-Timo Bremer: Lawrence Livermore National Laboratory
Todd Gamblin: Lawrence Livermore National Laboratory
Alfredo Gimenez: Lawrence Livermore National Laboratory
Kate Isaacs: Lawrence Livermore National Laboratory

A chapter in Tools for High Performance Computing 2014, 2015, pp 211-229 from Springer

Abstract: Abstract Performance data can be complex and potentially high dimensional. Further, it can be collected in multiple, independent domains. For example, one can measure code segments, hardware components, data structures, or an application’s communication structure. Performance analysis and visualization tools require access to this data in an easy way and must be able to specify relationships and mappings between these domains in order to provide users with intuitive, actionable performance analysis results. In this paper, we describe a data model that can represent such complex performance data, and we discuss how this model helps us to specify mappings between domains. We then apply this model to several use cases both for data acquisition and how it can be mapped into the model, and for performance analysis and how it can be used to gain insight into an application’s performance.

Keywords: Performance Data; Target Domain; Application Developer; Origin Domain; Float Point Operation (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-16012-2_10

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DOI: 10.1007/978-3-319-16012-2_10

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