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Performance Technology for Productive, High-End Parallel Computing

Performance Technology for Productive, High-End Parallel Computing. Allen D. Malony malony@cs.uoregon.edu Department of Computer and Information Science Performance Research Laboratory NeuroInformatics Center University of Oregon. Performance Tuning. Performance Technology. hypotheses.

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Performance Technology for Productive, High-End Parallel Computing

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  1. Performance Technology for Productive,High-End Parallel Computing Allen D. Malony malony@cs.uoregon.edu Department of Computer and Information Science Performance Research Laboratory NeuroInformatics Center University of Oregon

  2. PerformanceTuning PerformanceTechnology hypotheses Performance Diagnosis • Experimentmanagement • Performancestorage PerformanceTechnology properties Performance Experimentation • Instrumentation • Measurement • Analysis • Visualization characterization Performance Observation Research Motivation • Tools for performance problem solving • Empirical-based performance optimization process • Performance technology concerns

  3. Challenges in Performance Problem Solving • How to make the process more effective (productive)? • Process may depend on scale of parallel system • What are the important events and performance metrics? • Tied to application structure and computational model • Tied to application domain and algorithms • Process and tools can/must be more application-aware • Tools have poor support for application-specific aspects • What are the significant issues that will affect the technology used to support the process? • Enhance application development and benchmarking • New paradigm in performance process and technology

  4. Large Scale Performance Problem Solving • How does our view of this process change when we consider very large-scale parallel systems? • What are the significant issues that will affect the technology used to support the process? • Parallel performance observation is clearly needed • In general, there is the concern for intrusion • Seen as a tradeoff with performance diagnosis accuracy • Scaling complicates observation and analysis • Performance data size becomes a concern • Analysis complexity increases • Nature of application development may change

  5. Role of Intelligence, Automation, and Knowledge • Scale forces the process to become more intelligent • Even with intelligent and application-specific tools, the decisions of what to analyze is difficult and intractable • More automation and knowledge-based decision making • Build automatic/autonomic capabilities into the tools • Support broader experimentation methods and refinement • Access and correlate data from several sources • Automate performance data analysis / mining / learning • Include predictive features and experiment refinement • Knowledge-driven adaptation and optimization guidance • Address scale issues through increased expertise

  6. Outline of Talk • Performance problem solving • Scalability, productivity, and performance technology • Application-specific and autonomic performance tools • TAU parallel performance system and advances • Performance data management and data mining • Performance Data Management Framework (PerfDMF) • PerfExplorer • Multi-experiment case studies • Clustering analysis • Comparative analysis (PERC tool study) • Future work and concluding remarks

  7. TAU Performance System • Tuning and Analysis Utilities (13+ year project effort) • Performance system framework for HPC systems • Integrated, scalable, flexible, and parallel • Targets a general complex system computation model • Entities: nodes / contexts / threads • Multi-level: system / software / parallelism • Measurement and analysis abstraction • Integrated toolkit for performance problem solving • Instrumentation, measurement, analysis, and visualization • Portable performance profiling and tracing facility • Performance data management and data mining • University of Oregon , Research Center Jülich, LANL

  8. TAU Parallel Performance System Goals • Multi-level performance instrumentation • Multi-language automatic source instrumentation • Flexible and configurable performance measurement • Widely-ported parallel performance profiling system • Computer system architectures and operating systems • Different programming languages and compilers • Support for multiple parallel programming paradigms • Multi-threading, message passing, mixed-mode, hybrid • Support for performance mapping • Support for object-oriented and generic programming • Integration in complex software, systems, applications

  9. TAU Performance System Architecture event selection

  10. TAU Performance System Architecture

  11. Advances in TAU Instrumentation • Source instrumentation • Program Database Toolkit (PDT) • automated Fortran 90/95 support (Cleanscape Flint parser) • statement level support in C/C++ (Fortran soon) • TAU_COMPILER to automate instrumentation process • Automatic proxy generation for component applications • automatic CCA component instrumentation • Python instrumentation and automatic instrumentation • Continued integration with dynamic instrumentation • Update of OpenMP instrumentation (POMP2) • Selective instrumentation and overhead reduction • Improvements in performance mapping instrumentation

  12. Program Database Toolkit (PDT) Application / Library C / C++ parser Fortran parser F77/90/95 Program documentation PDBhtml Application component glue IL IL SILOON C / C++ IL analyzer Fortran IL analyzer C++ / F90/95 interoperability CHASM Program Database Files Automatic source instrumentation TAU_instr DUCTAPE

  13. Advances in TAU Measurement • Profiling (four types) • Memory profiling • global heap memory tracking (several options) • Callpath profiling and calldepth profiling • user-controllable callpath length and calling depth • Phase-based profiling • Tracing • Generation of VTF3 / SLOG traces files (fully portable) • Inclusion of hardware performance counts in trace files • Hierarchical trace merging • Online performance overhead compensation • Component software proxy generation and monitoring

  14. Profile Measurement • Flat profiles • Metric (e.g., time) spent in an event (callgraph nodes) • Exclusive/inclusive, # of calls, child calls • Callpath profiles (Calldepth profiles) • Time spent along a calling path (edges in callgraph) • “main=> f1 => f2 => MPI_Send” (event name) • TAU_CALLPATH_LENGTH environment variable • Phase-based profiles • Flat profiles under a phase (nested phases are allowed) • Default “main” phase • Supports static or dynamic (per-iteration) phases

  15. Advances in TAU Performance Analysis • Enhanced parallel profile analysis (ParaProf) • Callpath analysis integration in ParaProf • Event callgraph view • Performance Data Management Framework (PerfDMF) • First release of prototype • In use by several groups • S. Moore (UTK), P. Teller (UTEP), P. Hovland (ANL), … • Integration with Vampir Next Generation (VNG) • Online trace analysis • 3D Performance visualization prototype (ParaVis) • Component performance modeling and QoS

  16. Pprof – Flat Profile (NAS PB LU) • Intel Linux cluster • F90 + MPICH • Profile - Node - Context - Thread • Events - code - MPI • Metric - time • Text display

  17. ParaProf – Manager Window performancedatabase derived performance metrics

  18. ParaProf – Full Profile (Miranda) 8K processors!

  19. ParaProf– Flat Profile (Miranda)

  20. ParaProf– Callpath Profile (Flash)

  21. ParaProf– Callpath Profile (ESMF) 21-levelcallpath

  22. ParaProf – Phase Profile (MFIX) In 51st iteration, time spent in MPI_Waitall was 85.81 secs dynamic phases one per interation Total time spent in MPI_Waitall was 4137.9 secs across all 92 iterations

  23. ParaProf – Histogram View (Miranda) • Scalable 2D displays 16k processors 8k processors

  24. ParaProf –Callgraph View (MFIX)

  25. ParaProf – Callpath Highlighting (Flash) MODULEHYDRO_1D:HYDRO_1D

  26. Profiling of Miranda on BG/L (Miller, LLNL) • Profile code performance (automatic instrumentation) • Scaling studies (problem size, number of processors) • Run on 8K and 16K processors! 128 Nodes 512 Nodes 1024 Nodes

  27. ParaProf – 3D Full Profile (Miranda) 16k processors

  28. ParaProf – 3D Scatterplot (Miranda) • Each pointis a “thread”of execution • A total offour metricsshown inrelation • ParaVis 3Dprofilevisualizationlibrary • JOGL

  29. Performance Tracing on Miranda • Use TAU to generate VTF3 traces for Vampir analysis • MPI calls with HW counter information (not shown) • Detailed code behavior to focus optimization efforts

  30. S3D on Lemieux (TAU-to-VTF3, Vampir)

  31. S3D on Lemieux (Zoomed)

  32. TAU Performance System Status • Computing platforms (selected) • IBM SP/pSeries, SGI Origin, Cray T3E/SV-1/X1/XT3, HP (Compaq) SC (Tru64), Sun, Hitachi SR8000, NEC SX-5/6, Linux clusters (IA-32/64, Alpha, PPC, PA-RISC, Power, Opteron), Apple (G4/5, OS X), Windows • Programming languages • C, C++, Fortran 77/90/95, HPF, Java, OpenMP, Python • Thread libraries (selected) • pthreads, SGI sproc, Java,Windows, OpenMP • Compilers (selected) • Intel KAI (KCC, KAP/Pro), PGI, GNU, Fujitsu, Sun, PathScale, SGI, Cray, IBM (xlc, xlf), HP, NEC, Absoft

  33. Project Affiliations (selected) • Center for Simulation of Accidental Fires and Explosion • University of Utah, ASCI ASAP Center, C-SAFE • Uintah Computational Framework (UCF) (C++) • Center for Simulation of Dynamic Response of Materials • California Institute of Technology, ASCI ASAP Center • Virtual Testshock Facility (VTF) (Python, Fortran 90) • Earth Systems Modeling Framework (ESMF) • NSF, NOAA, DOE, NASA, … • Instrumentation for ESMF framework and applications • C, C++, and Fortran 95 code modules • MPI wrapper library for MPI calls

  34. Project Affiliations (selected) (continued) • Lawrence Livermore National Lab • Hydrodynamics (Miranda), Radiation diffusion (KULL) • Sandia National Lab and Los Alamos National Lab • DOE CCTTSS SciDAC project • Common component architecture (CCA) integration • Argonne National Lab • OS / RTS for Extreme Scale Scientific Computation • Zeptos - scalable components for petascale architectures • KTAU - integration of TAU infrastructure in Linux kernel • Oak Ridge National Lab • Contribution to the Joule Report: S3D, AORSA3D

  35. Important Questions for Application Developers • How does performance vary with different compilers? • Is poor performance correlated with certain OS features? • Has a recent change caused unanticipated performance? • How does performance vary with MPI variants? • Why is one application version faster than another? • What is the reason for the observed scaling behavior? • Did two runs exhibit similar performance? • How are performance data related to application events? • Which machines will run my code the fastest and why? • Which benchmarks predict my code performance best?

  36. Performance Problem Solving Goals • Answer questions at multiple levels of interest • Data from low-level measurements and simulations • use to predict application performance • High-level performance data spanning dimensions • machine, applications, code revisions, data sets • examine broad performance trends • Discover general correlations application performance and features of their external environment • Develop methods to predict application performance on lower-level metrics • Discover performance correlations between a small set of benchmarks and a collection of applications that represent a typical workload for a given system

  37. Performancedatabase Automatic Performance Analysis Tool (Concept) 105% Faster! 72% Faster! Simpleanalysisfeedback Build application Execute application environment /performancedata build information Offline analysis

  38. Performance Data Management Framework

  39. ParaProf Performance Profile Analysis Raw files HPMToolkit PerfDMFmanaged (database) Metadata MpiP Application Experiment Trial TAU

  40. PerfExplorer (K. Huck, Ph.D. student, UO) • Performance knowledge discovery framework • Use the existing TAU infrastructure • TAU instrumentation data, PerfDMF • Client-server based system architecture • Data mining analysis applied to parallel performance data • comparative, clustering, correlation, dimension reduction, ... • Technology integration • Relational DatabaseManagement Systems (RDBMS) • Java API and toolkit • R-project / Omegahat statistical analysis • WEKA data mining package • Web-based client

  41. PerfExplorer Architecture

  42. PerfExplorer Client GUI

  43. Hierarchical and K-means Clustering (sPPM)

  44. Miranda Clustering on 16K Processors

  45. PERC Tool Requirements and Evaluation • Performance Evaluation Research Center (PERC) • DOE SciDAC • Evaluation methods/tools for high-end parallel systems • PERC tools study (led by ORNL, Pat Worley) • In-depth performance analysis of select applications • Evaluation performance analysis requirements • Test tool functionality and ease of use • Applications • Start with fusion code – GYRO • Repeat with other PERC benchmarks • Continue with SciDAC codes

  46. Primary Evaluation Machines • Phoenix (ORNL – Cray X1) • 512 multi-streaming vector processors • Ram (ORNL – SGI Altix (1.5 GHz Itanium2)) • 256 total processors • TeraGrid • ~7,738 total processors on 15 machines at 9 sites • Cheetah (ORNL – p690 cluster (1.3 GHz, HPS)) • 864 total processors on 27 compute nodes • Seaborg (NERSC – IBM SP3) • 6080 total processors on 380 compute nodes

  47. GYRO Execution Parameters • Three benchmark problems • B1-std : 16n processors, 500 timesteps • B2-cy : 16n processors, 1000 timesteps • B3-gtc : 64n processors, 100 timesteps (very large) • Test different methods to evaluate nonlinear terms: • Direct method • FFT (“nl2” for B1 and B2, “nl1” for B3) • Task affinity enabled/disabled (p690 only) • Memory affinity enabled/disabled (p690 only) • Filesystem location (Cray X1 only)

  48. PerfExplorer Analysis of Self-Instrumented Data • PerfExplorer • Focus on comparative analysis • Apply to PERC tool evaluation study • Look at user timer data • Aggregate data • no per process data • process clustering analysis is not applicable • Timings output every N timesteps • some phase analysis possible • Goal • Recreate manually generated performance reports

  49. PerfExplorer Interface Experimentmetadata Select experiments and trials of interest Data organized in application, experiment, trial structure (will allow arbitrary in future)

  50. PerfExplorer Interface Select analysis

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