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Faculty Research Colloquium Prof. Allen Davis Malony’s Research

Faculty Research Colloquium Prof. Allen Davis Malony’s Research. Formal Introduction (at short notice) by Aroon Nataraj Department of Computer and Information Science University of Oregon. Outline of the Formal Introduction. The Good, Parallel Performance Tools Neuroinformatics

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Faculty Research Colloquium Prof. Allen Davis Malony’s Research

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  1. Faculty Research ColloquiumProf. Allen Davis Malony’sResearch Formal Introduction (at short notice) by Aroon Nataraj Department of Computer and Information Science University of Oregon

  2. Outline of the Formal Introduction • The Good, • Parallel Performance Tools • Neuroinformatics • The Bad and • The Ugly • Research group members • Conclusion

  3. The Good - Research Areas • High-level Research Areas • Parallel Performance Tools • Neuroinformatics • Parallel Performance Tools • Writing parallel applications is hard (because we don’t usually think in parallel terms). Getting them to perform well is harder. • Many issues affecting performance (not all specific to parallel processing) • Application algorithms, communication patterns, middleware, system software (including OS), underlying machine architecture • As scale becomes larger (100,000+ cores), all of these (and many other) influences start to matter • Overall goal: Provide a portable, scalable, low-overhead framework to observe, analyze, visualize, manage and diagnose parallel performance problems

  4. The Good - Parallel Performance Tools • Instrumentation • Placing performance collection probes inside an application • Manual, Automatic Source, Binary, Runtime Dynamic • PDT - Program Database Toolkit - Aids in automatic instrumentation • Metrics • What are we interested in calculating? Time is the primary concern. • Cache misses, FP operations, OS operations, Network metrics, Wait-states • Measurement • We have events and metrics, what do we do with those? • Place it in a large buffer for post-mortem processing (Tracing)? • Cull more sophisticated performance information at run-time such as recreating the call-tree and identifying costly paths? (Different types of Profiling) • TAU - Tuning and Analysis Utilities - Umbrella project • Analysis • Given traces or profiles, what higher-level analyses can we run on them? • Can we mine the data looking for patterns (Data Mining)? • PerfExplorer - Clustering, Correlation, Dimension Reduction (PCA, etc)

  5. The Good - Parallel Performance Tools • Visualization • Presentation of the (analyzed) performance data in meaningful ways that allow identification of performance problems • ParaProf - Parallel Profile Visualization (Also PerfExplorer) • Vampir/Jumpshot adaptors for trace visualization • Correlating System-level Influences • OS-level influences becoming very important in large-scale parallel environments • Extending TAU technology into the OS kernel and correlating OS-events closely with application performance • KTAU - Kernel TAU Project - Linux instrumentation, measurement, analyses • Online Monitoring and Feedback • Performance observation and analyses not limited to post-mortem • Long-running parallel applications need to track performance • TAUg (TAU Global View), TAUoverSupermon (ToS), TAUoverMRNET (ToM) • Model-based Automatic Performance Diagnosis • How to extract performance knowledge from parallel models (e.g. master-worker)? • How to represent the knowledge in such a way that diagnosis can be carried out in an automatic manner? • Hercule - a prototype automatic performance diagnosis system

  6. Screenshots! ParaProf Call-Path • Simple example • Can handle arbitrary (recursive) call-paths • Different presentations possible

  7. Screenshots! ParaProf Scalability • Histogram View of Thousands of processors 8k processors 16k processors

  8. Screenshots! ParaProf 3-D Views • Histogram View of Thousands of processors 16k processors

  9. Screenshots! PerfExplorer Correlation Analysis • Describes strength and direction of a linear relationship between two variables (events) in the data

  10. KTAU: Correlating OS events with Application • OS level event tracking on the Blue Gene/L Supercomputer

  11. The Good - Neuroinformatics Research • Neuroinformatics research at the Neuroinformatics Center (NIC) • Dense-array EEG analysis (APECS, HiPerSAT) • Brain image segmentation • Computational head modeling • Ontologies and tool integration (NEMO) • Parallel and distributed computing emphasis • Application of computational science methods to human neuroscience problems • Tools to help understand dynamic brain function • Tools to help diagnosis brain-related disorders • HPC simulation, large-scale data analysis, visualization • Integration of neuroimaging methods and technology • Need for coupled modeling (EEG/ERP, MR analysis) • Apply advanced statistical signal analysis (PCA, ICA) • Develop computational brain models (FDM, FEM) • Build source localization models (dipole, linear inverse)

  12. The Good - Neuroinformatics • Neuroinformatics research at the Neuroinformatics Center (NIC) • Dense-array EEG analysis (APECS, HiPerSAT) • Brain image segmentation • Computational head modeling • Ontologies and tool integration (NEMO) • Parallel and distributed computing emphasis • Application of computational science methods to human neuroscience problems • Tools to help understand dynamic brain function • Tools to help diagnosis brain-related disorders • HPC simulation, large-scale data analysis, visualization • Integration of neuroimaging methods and technology • Need for coupled modeling (EEG/ERP, MR analysis) • Apply advanced statistical signal analysis (PCA, ICA) • Develop computational brain models (FDM, FEM) • Build source localization models (dipole, linear inverse)

  13. Neuroinformatics @ NIC - Pictures! • Notice how everybody looks peaceful

  14. The Bad He is bad alright. Real bad. But is there any hard evidence? (Since we are researchers, we need data.) You bet!

  15. The Bad (evidence) Many-Time Winner Oregon Muscleman Competition

  16. The Bad (more evidence) A veritable master of disguise You may not believe it - But that is him! Really!

  17. The Ugly His current website:http://www.cs.uoregon.edu/~malony Pay attention to the date.

  18. The Group • The current group at UO • Prof. Allen Malony • Sameer Shende, Matt Sottile, Alan Morris, Wyatt Spear, Scott Biersdorf, Brandon Davidson (Full-time PRL) • Kevin Huck, Adnan Salman, Aroon Nataraj (Students) • New Members • Geoff Hulette, Shangkar Mayanglambam (Students) • Recent Graduates • Kai Li, PhD (Neuroinformatics) • Li Li, PhD (Model-based diagnosis) at ANL • Suravee S., Masters at AMD • Partners: LLNL, ANL, LANL, Research Center Jülich

  19. Thanks You • Courses taught by Dr.Malony in the recent past: • Advanced Operating Systems / Distributed Systems • Parallel Programming • Multicore Seminar • You can contact Dr. Malony at: • malony@cs.uoregon.edu

  20. KTAU: Correlating OS events with Application • Courses taught by Dr.Malony in the recent past: • Advanced Operating Systems / Distributed Systems • Parallel Programming • Multicore Seminar • You can contact Dr. Malony at: • malony@cs.uoregon.edu

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