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PerfExplorer Component for Performance Data Analysis

PerfExplorer Component for Performance Data Analysis. Kevin Huck – University of Oregon Boyana Norris – Argonne National Lab Li Li – Argonne National Lab. PerfDMF. Perf ormance D ata M anagement F ramework Provides profile data management

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PerfExplorer Component for Performance Data Analysis

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  1. PerfExplorer Component for Performance Data Analysis Kevin Huck – University of Oregon Boyana Norris – Argonne National Lab Li Li – Argonne National Lab CCA-Salishan April, 2008

  2. PerfDMF • Performance Data Management Framework • Provides profile data management • Database support: MySQL, PostgreSQL, Derby, Oracle, DB2 • Parsers/Importers: TAU, Dynaprof, mpiP, gprof, psrun (PerfSuite), HPCToolkit (Rice), HPC Toolkit (IBM), CUBE (KOJAK), OpenSpeedShop, GPTL, application timers • Profile query and analysis API CCA-Salishan April, 2008

  3. PerfExplorer • Built on PerfDMF • Framework for systematic, collaborative and reusable parallel performance analysis • Large-scale performance analysis for single experiments on thousands of processors • Multiple experiments from parametric studies • Addresses the need for complexity management • Clean interface to existing tools for easy access to analysis and data mining (Weka, R) • Abstraction/automation of data mining operations CCA-Salishan April, 2008

  4. PerfExplorer 2.0 • “Component”-based analysis • Provides access analysis operations & data from scripts • Scripting • Provides analysis automation • Metadata Support • Inference engine • To reason about causes of performance phenomena from expert rules • Persistence of intermediate results • Provenance • Provides historical record of analysis results CCA-Salishan April, 2008

  5. PerfExplorer 2.0 “Components” CCA-Salishan April, 2008

  6. PerfExplorer 2.0 Design with CCA CCA Component Interface CCA-Salishan April, 2008

  7. PerfExplorer CCA Component • First Goal – support for CQoS • Choosing linear solver and parameters for iterative non-linear solver, based on input data and minimizing time to solution (time, iterations) • No interfaces defined yet… • Just getting started with CCA, modifying PE2 • No GUI, parse Li’s tables, support User Events • Planned analysis methods • Simple regression (linear and non-linear) • Machine Learning methods • Support Vector Regression: there is Weka support. • Genetic Algorithms: there may be Weka support. CCA-Salishan April, 2008

  8. Acknolwedgements • University of Oregon • Prof. Allen Malony • Dr. Sameer Shende • Matt Sottile • Alan Morris • Argonne National Lab • Boyana Norris • Li Li CCA-Salishan April, 2008

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