1. PerfExplorer Component for Performance Data Analysis Kevin Huck – University of Oregon Boyana Norris – Argonne National Lab Li Li – Argonne National Lab 11/23/20151CCA-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 11/23/20152CCA-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 11/23/20153CCA-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 11/23/20154CCA-Salishan April, 2008

5. PerfExplorer 2.0 “Components” Basic StatisticsExtract eventsTop X events CopyExtract metricsTop X percent events CorrelationExtract phasesANOVA Correlation with metadataExtract rankLinear regression Derive metricsk-meansNon-linear regression* DifferenceMerge trialsBackward elimination* Extract callpath eventsPCACorrelation elimination* Extract non-callpath eventsScalability* future development 11/23/20155CCA-Salishan April, 2008

6. PerfExplorer 2.0 Design with CCA CCA Component Interface 11/23/20156CCA-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. 11/23/20157CCA-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 11/23/20158CCA-Salishan April, 2008