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PerfExplorer Component for Performance Data Analysis

PerfExplorer 2.0 is a cutting-edge component-based analysis tool built on PerfDMF, offering systematic and collaborative performance analysis for large-scale experiments on thousands of processors. It provides automation, metadata support, and historical record of analysis results. This tool enables the exploration and extraction of valuable insights from performance data efficiently and effectively.

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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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