Component Architectures

Transcription

1 Component Architectures Rapid Prototyping in a Networked Environment Ken Kennedy Rice University

2 Participants Ruth Aydt Bradley Broom Zoran Budimlic Barbara Chapman Keith Cooper Jack Dongarra Rob Fowler Richard Hanson Lennart Johnsson Ken Kennedy John Mellor-Crummey Dan Reed Jaspal Subhlok Linda Torczon

3 Areas of Activity Compilation of Object-Oriented Languages Java for high performance computing Using the full power of the language Implementation of High-Level Domain-Specific Languages Telescoping languages Framework for generating fast scripting systems from libraries Numerical component libraries Automatic tuning of libraries Application Development Support for Computational Grids The GrADS Project Strategy Optimal resource selection in grids

4 Java Compilation Strategy Secure Java Environment Java Program JaMake Environment Java Front End Whole-Program Optimization Java Optimizer Java Bytecodes Source-to-Source Transformation Bytecode Compiler Conventional Optimizing Compiler Java VM with JIT Native Machine Code

5 Java Compilation Strategy Secure Java Environment Java Program JaMake Environment Java Front End Whole-Program Optimization Java Optimizer Java Bytecodes Source-to-Source Transformation Bytecode Compiler Conventional Optimizing Compiler Java VM with JIT Native Machine Code

6 JaMake: A Java Transformation Tool Source-to-Source Transformations Works on any JVM Can be viewed as a packaging tool Extensive Use of Interprocedural Analysis and Optmization Whole-program transformation framework Employs powerful type analysis Almost-whole-program transformations Innovative New Optimizations Object inlining Inlines whole objects, including data Converts array of objects to arrays of built-in types Class specialization Specializes by instantiaton type of class data

7 Making Languages Usable It was our belief that if FORTRAN, during its first months, were to translate any reasonable scientific source program into an object program only half as fast as its hand-coded counterpart, then acceptance of our system would be in serious danger... I believe that had we failed to produce efficient programs, the widespread use of languages like FORTRAN would have been seriously delayed. John Backus

8 A Java Experiment Scientific Programming In Java Goal: make it possible to use the full object-oriented power for scientific applications Many scientific implementations mimic Fortran style

9 A Java Experiment Scientific Programming In Java Goal: make it possible to use the full object-oriented power for scientific applications Many scientific implementations mimic Fortran style OwlPack Benchmark Suite Three versions of LinPACK in Java Fortran style Lite object-oriented style Full polymorphism No differences for type

10 A Java Experiment Scientific Programming In Java Goal: make it possible to use the full object-oriented power for scientific applications Many scientific implementations mimic Fortran style OwlPack Benchmark Suite Three versions of LinPACK in Java Fortran style Lite object-oriented style Full polymorphism Experiment No differences for type Compare running times for different styles on same Java VM Evaluate potential for compiler optimization

11 Performance Results 100 Sun Ultra dpofa dposl dpodi dgefa dgesl dgedi dqrdc dqrsl dsvdc average 1.75 Fortran style Lite OO OO style Optimized OO

12 Plans New Global Type Analysis Implementation Precise global analysis derived from call-graph construction strategies Work with the CartaBlanca Group Determine whether JaMake can improve overall performance Experiment with Telescoping Languages Strategies Improved performance with almost-whole program analysis Explore Collaboration with JVM Jalapeño project at IBM Research Continued collaboration with Compaq

13 Programming Productivity Challenges programming is hard professional programmers are in short supply high performance will continue to be important

14 Programming Productivity Challenges programming is hard professional programmers are in short supply high performance will continue to be important One Strategy: Make the End User a Programmer professional programmers develop components users integrate components using: problem-solving environments (PSEs) scripting languages (possibly graphical) examples: Visual Basic, Tcl/Tk, AVS, Khoros

15 Programming Productivity Challenges programming is hard professional programmers are in short supply high performance will continue to be important One Strategy: Make the End User a Programmer professional programmers develop components users integrate components using: problem-solving environments (PSEs) scripting languages (possibly graphical) examples: Visual Basic, Tcl/Tk, AVS, Khoros Compilation for High Performance translate scripts and components to common intermediate language optimize the resulting program using interprocedural methods

16 Script-Based Programming Component Library User Library Script

17 Script-Based Programming Component Library User Library Translator Intermediate Code Script

18 Script-Based Programming Component Library Global Optimizer User Library Translator Intermediate Code Script

19 Script-Based Programming Component Library Global Optimizer User Library Translator Intermediate Code Code Generator Script

20 Script-Based Programming Component Library Global Optimizer User Library Translator Intermediate Code Code Generator Script Problem: long compilation times, even for short scripts!

21 Script-Based Programming Component Library Global Optimizer User Library Translator Intermediate Code Code Generator Script Problem: long compilation times, even for short scripts! Problem: expert knowledge on specialization lost

22 Telescoping Languages L 1 Class 1 Library

23 Telescoping Languages L 1 Class 1 Library Compiler Generator Could run for hours L 1 Compiler 1

24 Telescoping Languages L 1 Class 1 Library Compiler Generator Could run for hours Script Script Translator L 1 Compiler 1 understands library calls as primitives Vendor Compiler Optimized Application

25 Telescoping Languages: Advantages Compile times can be reasonable More compilation time can be spent on libraries Script compilations can be fast Components reused from scripts may be included in libraries High-level optimizations can be included Based on specifications of the library designer Properties often cannot be determined by compilers Properties may be hidden after low-level code generation User retains substantive control over language performance Mature code can be built into a library and incorporated into language Reliability can be improved Specialization by compilation framework, not user

26 Applications Matlab Compiler Automatically generated from LAPACK or ScaLAPACK With help via annotations from the designer Flexible Data Distributions Failing of HPF: inflexible distributions Data distribution == collection of interfaces that meet specs Compiler applies standard transformations Automatic Generation of POOMA Data structure library implemented via template expansion in C++ Long compile times, missed optimizations Generator for Grid Computations GrADS: automatic generation of NetSolve Hardware Synthesis Languages

27 Application: Matlab for Signal Processing Signal processing users want simplicity, programming power, and performance Currently over 500,000 Matlab licenses Matlab gives them simplicity and power but not performance Codes prototyped in Matlab Codes rewritten in C for communications devices Users would rather not do this Telescoping Languages: Many signal processing code modules reused over and over Run these procedures through the language generator Produce Matlab SP, a high-level domain-specific environment

28 Matlab SP: Preliminary Findings Optimizations That Pay Off Vectorization Wins because of hand coded vector/matrix primitives Elimination of common array subexpressions Optimization of array allocation and reshape operations New Optimizations Procedure vectorization Interchange call and loop after distribution Procedure strength reduction Subdivide procedure in to variant and invariant components Use invariant component only once

29 Procedure Strength Reduction Procedure called in loop for i = 1:N x = f(c 1,c 2,i,c 3 ) end Becomes f (c µ 1,c 2, c 3 ) for i = 1:N x = f (i) end Further improvements possible Use code differentiation to compute differences ADIFOR

30 Procedure Strength Reduction Performance jmp1 newcd cdsdhd ctss olbf Original Optimized

31 Component Libraries Components for Use in High-Level Domain-Specific Languages Telescoping-language-ready libraries Grid-aware components Automatic Tuning of Kernels Atlas, UHFFT Discussed in talks by Dongarra, Johnsson Automatic Application Tuning Generation of tuning search for arbitrary loop nests

32 National Distributed Computing

33 National Distributed Computing

34 National Distributed Computing Supercomputer

35 National Distributed Computing Supercomputer Database

36 National Distributed Computing Supercomputer Database Supercomputer

37 National Distributed Computing Database Supercomputer Database Supercomputer

38 Globus Developed by Ian Foster and Carl Kesselman Originally to support the I-Way (SC-96) Basic Services for distributed computing Accounting Resource directory and other information services User authentication Job initiation Communication services (Nexus and MPI) Applications are programmed by hand User responsible for resource mapping and all communication Many applications, most developed with Globus team Globus developers acknowledge how hard this is

39 GrADSoft Architecture Goal: reliable performance under varying load Performance Feedback Software Components Performance Problem Real-time Performance Monitor Whole- Program Compiler Source Application Configurable Object Program Service Negotiator Scheduler Negotiation Grid Runtime System Libraries Dynamic Optimizer GrADS Project (NSF NGS): Berman, Chien, Cooper, Dongarra, Foster, Gannon Johnsson, Kennedy, Kesselman, Mellor-Crummey, Reed, Torczon, Wolski

40 GrADSoft Architecture Execution Environment Performance Feedback Software Components Performance Problem Real-time Performance Monitor Whole- Program Compiler Source Application Configurable Object Program Service Negotiator Scheduler Negotiation Grid Runtime System Libraries Dynamic Optimizer

41 GrADSoft Architecture Program Preparation System Execution Environment Performance Feedback Software Components Performance Problem Real-time Performance Monitor Whole- Program Compiler Source Application Configurable Object Program Service Negotiator Scheduler Negotiation Grid Runtime System Libraries Dynamic Optimizer

42 Grid Activities Support for GrADS Investigation Making the application development strategy generic Compilation of configurable object programs Construction of Accurate Performance Models Graph-based resource selection Accurate inference of performance characteristics on specific collections of resources Inference of communication characteristics Grid Utilities New algorithm for Grid broadcast Future Collaborate on a LANL application

43 Summary Applications of Component Architectures Programming productivity Grid programming systems Reliability General Issue: Performance Without it, usage will be discouraged Strategies for Overcoming Performance Issues Speculative compilation Use of extensive computation to tune applications to different platforms Adaptation at run time