Performance Technology for Productive, High-End Parallel Computing

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 University of Oregon

2. Outline of Talk • Research motivation • Scalability, productivity, and performance technology • Application-specific and autonomic performance tools • TAU parallel performance system developments • Application performance case studies • New project directions • Performance data mining and knowledge discovery • Concluding discussion

3. PerformanceTuning PerformanceTechnology hypotheses Performance Diagnosis • Experimentmanagement • Performancedatabase 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

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 • Nature of application development may change • Paradigm shift in performance process and technology? • What will enhance productive application development?

5. Scaling and Performance Observation • Consider “traditional” measurement methods • Profiling: summary statistics calculated during execution • Tracing: time-stamped sequence of execution events • More parallelism  more performance data overall • Performance specific to each thread of execution • Possible increase in number interactions between threads • Harder to manage the data (memory, transfer, storage) • How does per thread profile size grow? • Instrumentation more difficult with greater parallelism? • More parallelism / performance data  harder analysis • More time consuming to analyze and difficult to visualize

6. Concern for Performance Measurement Intrusion • Performance measurement can affect the execution • Perturbation of “actual” performance behavior • Minor intrusion can lead to major execution effects • Problems exist even with small degree of parallelism • Intrusion is accepted consequence of standard practice • Consider intrusion (perturbation) of trace buffer overflow • Scale exacerbates the problem … or does it? • Traditional measurement techniques tend to be localized • Suggests scale may not compound local intrusion globally • Measuring parallel interactions likely will be affected • Use accepted measurement techniques intelligently

7. Role of Intelligence and Specificity • How to make the process more effective (productive)? • Scale forces performance observation to be intelligent • Standard approaches deliver a lot of data with little value • What are the important performance events and data? • Tied to application structure and computational mode • Tools have poor support for application-specific aspects • Process and tools can be more application-aware • Will allow scalability issues to be addressed in context • More control and precision of performance observation • More guided performance experimentation / exploration • Better integration with application development

8. Role of Automation and Knowledge Discovery • Even with intelligent and application-specific tools, the decisions of what to analyze may become intractable • Scale forces the process to become more automated • Performance extrapolation must be part of the process • Build 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

9. 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

10. TAU Parallel Performance System Architecture

11. TAU Parallel Performance System Architecture

12. Advances in TAU Instrumentation • Source instrumentation • Program Database Toolkit (PDT) • automated Fortran 90/95 support (Flint parser, very robust) • 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

13. Advances in TAU Measurement • Profiling • Memory profiling • global heap memory tracking (several options) • Callpath profiling • user-controllable calling depth • Improved support for multiple counter profiling • Online profile access and sampling • Tracing • Generation of VTF3 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. Advances in TAU Performance Analysis • Enhanced parallel profile analysis (ParaProf) • Callpath analysis integration in ParaProf • Embedded Lisp interpreter • 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 • Performance visualization (ParaVis) prototype • Component performance modeling and QoS

15. TAU Performance System Status • Computing platforms (selected) • IBM SP / pSeries, SGI Origin 2K/3K, Cray T3E / SV-1 / X1, 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 • pthreads, SGI sproc, Java,Windows, OpenMP • Compilers (selected) • Intel KAI (KCC, KAP/Pro), PGI, GNU, Fujitsu, Sun, Microsoft, SGI, Cray, IBM (xlc, xlf), HP, NEC, Absoft

16. Component-Based Scientific Applications • How to support performance analysis and tuning process consistent with application development methodology? • Common Component Architecture (CCA) applications • Performance tools should integrate with software • Design performance observation component • Measurement port and measurement interfaces • Build support for application component instrumentation • Interpose a proxy component for each port • Inside the proxy, track caller/callee invocations, timings • Automate the process of proxy component creation • using PDT for static analysis of components • include support for selective instrumentation

17. Flame Reaction-Diffusion (Sandia, J. Ray) CCAFFEINE

18. Component Modeling and Optimization • Given a set of components, where each component has multiple implementations, what is the optimal subset of implementations that solve a given problem? • How to model a single component? • How to model a composition of components? • How to select optimal subset of implementations? • A component only has performance meaning in context • Applications are dynamically composed at runtime • Application developers use components from others • Instrumentation may only be at component interfaces • Performance measurements need to be non-intrusive • Users interested in a coarse-grained performance

19. MasterMind Component (Trebon, IPDPS 2004)

20. Proxy Generator for other Applications • TAU (PDT) proxy component for: • QoS tracking [Boyana, ANL] • Debugging Port Monitor for CCA (tracks arguments) • SCIRun2 Perfume components [Venkat, U. Utah] • Exploring Babel for auto-generation of proxies: • Direct SIDL-to-proxy code generation • Generating client component interface in C++ • Using PDT for generating proxies

21. Earth Systems Modeling Framework • Coupled modeling with modular software framework • Instrumentation for ESMF framework and applications • PDT automatic instrumentation • Fortran 95 code modules • C / C++ code modules • MPI wrapper library for MPI calls • ESMF Component instrumentation (using CCA) • CCA measurement port manual instrumentation • Proxy generation using PDT and runtime interposition • Significant callpath profiling used by ESMF team

22. Using TAU Component in ESMF/CCA

23. TAU’s Paraprof Profile Browser (ESMF Data) Callpath profile

24. CUBE Browser (UTK, FZJ) (ESMF Data) location metric calltree TAU profiledata convertedto CUBE form

25. TAU Traces with Counters (ESMF)

26. Visualizing TAU Traces with Counters/Samples

27. F Uintah Computational Framework (UCF) • University of Utah, Center for Simulation of AccidentalFires and Explosions (C-SAFE), DOE ASCI Center • UCF analysis • Scheduling • MPI library • Components • Performancemapping • Use for onlineand offlinevisualization • ParaVis tools 500 processes

28. Scatterplot Displays (UCF, 500 processes) • Each pointcoordinatedeterminedby threevalues: MPI_Reduce MPI_Recv MPI_Waitsome • Min/Maxvalue range • Effective forclusteranalysis Relation between MPI_Recv and MPI_Waitsome

29. Online Unitah Performance Profiling • Demonstration of online profiling capability • Multiple profile samples • Each profile taken at major iteration (~ 60 seconds) • Colliding elastic disks • Test material point method (MPM) code • Executed on 512 processors ASCI Blue Pacific at LLNL • Example • 3D bargraph visualization • MPI execution time • Performance mapping • Multiple time steps

30. Online Unitah Performance Profiling

31. Miranda Performance Analysis (Miller, LLNL) • Miranda is a research hydrodynamics code • Fortran 95, MPI • Mostly synchronous • MPI_ALLTOALL on Np x,y communicators • Some MPI reductions and broadcasts for statistics • Good communications scaling • ACL and MCR Linux cluster • Up to 1728 CPUs • Fixed workload per CPU • Ported to BlueGene/L • Breaking News! (see next slide)

32. 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 this week! 128 Nodes 512 Nodes 1024 Nodes

33. Fine Grained Profiling via Tracing on Miranda • Use TAU to generate VTF3 traces for Vampir analysis • Combines MPI calls with HW counter information • Detailed code behavior to focus optimization efforts

34. Memory Usage Analysis • BG/L will have limited memory per node (512 MB) • Miranda uses TAU to profile memory usage • Streamlines code • Squeeze largerproblems on themachine • TAU’s footprintis small • Approximately100 bytes per eventper thread Max Heap Memory (KB) used for 1283 problemon 16 processors of ASC Frost at LLNL

35. Kull Performance Optimization (Miller, LLNL) • Kull is a Lagrange hydrodynamics code • Physics packages written in C++ and Fortran • Parallel Python interpreter run-time environment! • Scalar test problem analysis • Serial execution to identify performance factors • Original code profile indicated expensive functions • CCSubzonalEffects member functions • Examination revealed optimization opportunities • Loop merging • Amortizing geometric lookup over more calculations • Apply to CSSubzonalEffects member functions

36. Kull Optimization • CSSubzonalEffects member functions total time • Reduced from 5.80 seconds to 0.82 seconds • Overall run time reduce from 28.1 to 22.85 seconds Original Exclusive Profile Optimized Exclusive Profile

37. 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?

38. Multi-Level Performance Data Mining • New (just forming) research project • PSU : Karen L. Karavanic • Cornell : Sally A. McKee • UO : Allen D. Malony and Sameer Shende • LLNL : John M. May and Bronis R. de Supinski • Develop performance data mining technology • Scientific applications, benchmarks, other measurements • Systematic analysis for understanding and prediction • Better foundation for evaluation of leadership-class computer systems • “Scalable, Interoperable Tools to Support Autonomic Optimization of High-End Applications,” S. McKee, G. Tyson, A. Malony, begin Nov. 1, 2004.

39. General Goals • Answer questions at multiple levels of interest • Data from low-level measurements and simulations • use to predict application performance • data mining applied to optimize data gathering process • High-level performance data spanning dimensions • Machine, applications, code revisions • Examine broad performance trends • Need technology • Performance instrumentation and measurement • Performance data management • Performance analysis and results presentation • Automated performance experimentation and exploration

40. Specific Goals • Design, develop, and populate a performance database • 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 give system • Performance data mining infrastructure is important for all of these goals  Establish a more rational basis for evaluating the performance of leadership-class computers

41. PerfTrack: Performance DB and Analysis Tool PSU: Kathryn Mohror, Karen Karavanic UO: Kevin Huck LLNL: John May, Brian Miller (CASC)

42. TAU Performance Data Management Framework

43. TAU Performance Regression (PerfRegress) • Prototype developed by Alan Morris for Uintah • Re-implement using PerfDMF

44. Background – Ahn & Vetter, 2002 • “Scalable Analysis Techniques for Microprocessor Performance Counter Metrics,” SC2002 • Applied multivariate statistical analysis techniques to large datasets of performance data (PAPI events) • Cluster Analysis and F-Ratio • Agglomerative Hierarchical Method - dendogram identified groupings of master, slave threads in sPPM • K-means clustering and F-ratio - differences between master, slave related to communication and management • Factor Analysis • shows highly correlated metrics fall into peer groups • Combined techniques (recursively) leads to observations of application behavior hard to identify otherwise

45. n i=0 Similarity Analysis • Can we recreate Ahn and Vetter’s results? • Apply techniques from the phase analysis (Sherwood) • Threads of execution can be compared for similarity • Threads with abnormal behavior show up as less similar • Each thread is represented as a vector (V) of dimension n • n is the number of functions in the application V = [f1, f2, …, fn] (represent event mix) • Each value is the percentage of time spent in that function • normalized from 0.0 to 1.0 • Distance calculated between the vectors U and V: ManhattanDistance(U, V) = ∑ |ui - vi|

46. sPPM on Blue Horizon (64x4, OpenMP+MPI) • TAU profiles • 10 events • PerfDMF • threads 32-47

47. sPPM on MCR (total instructions, 16x2) • TAU/PerfDMF • 120 events • master (even) • worker (odd)

48. sPPM on MCR (PAPI_FP_INS, 16x2) • TAU profiles • PerfDMF • master/worker • higher/lower Same result as Ahn/Vetter

49. sPPM on Frost (PAPI_FP_INS, 256 threads) • View of fewer than half of the threads of execution is possible on the screen at one time • Three groups are obvious: • Lower ranking threads • One unique thread • Higher ranking threads • 3% more FP • Finding subtle differences is difficult with this view

50. sPPM on Frost (PAPI_FP_INS, 256 threads) • Dendrogram shows 5 natural clusters: • Unique thread • High ranking master threads • Low ranking master threads • High ranking worker threads • Low ranking worker threads • TAU profiles • PerfDMF • R direct access to DM • R routine threads