Aomatic Parallelization

ISBN-13: 9783528054014 / Angielski / Miękka / 1994 / 224 str.

Christoph W. Kessler

Opis

This work contains 11 contributions which deal with true automatic parallelization, and the focus is on automatic methods. Some of the questions under discussion are: up to which degree is automatic parallelization for DMS possible today? What are currently the most important problems for automatic parallelization and which new ideas are there? In which cases can knowledge-based methods help? Are there promising methods for automatic data distribution and redistribution? Why is performance prediction problematic?

Spis treści

2 The Weight Finder — An Advanced Profiler for Fortran Programs.- 2.1 Introduction.- 2.2 Prerequisite.- 2.3 The Weight Finder.- 2.3.1 Choosing sequential program parameters.- 2.3.2 Instrumentation.- 2.3.3 Optimization.- 2.3.4 Compile and Execute.- 2.3.5 Attribute and Visualize.- 2.4 Adaptation of Profile Data.- 2.4.1 Program transformations.- 2.4.2 Problem Size.- 2.5 Conclusion and Future Work.- 3 Predicting Execution Times of Sequential Scientific Kernels.- 3.1 Motivation.- 3.2 Deriving time formulae for code fragments.- 3.3 Obtaining a platform model.- 3.4 Examples.- 3.4.1 Fragment A.- 3.4.2 Fragment B.- 3.4.3 Fragment C.- 3.4.4 Fragment D.- 3.4.5 Fragment E.- 3.4.6 Fragment F.- 3.4.7 Summary of results.- 3.5 Discussion and Further Work.- 4 Isolating the Reasons for the Performance of Parallel Machines on Numerical Programs.- 4.1 Introduction.- 4.2 Micro Measurements.- 4.2.1 Micro Measurements for a Node Processor.- 4.2.2 Micro Measurements for Communication Networks.- 4.3 Measurements.- 4.3.1 Measurements of the Serial Kernels.- 4.3.2 Measurements of the Parallel Kernels.- 4.4 Algorithms.- 4.4.1 CG—method.- 4.4.2 PDE1—method.- 4.4.3 PDE2—method.- 4.5 Analysis of the Programs.- 4.5.1 Serial Versions.- 4.5.2 Parallel Versions.- 4.6 Conclusion.- 5 Targeting Transputer Systems, Past and Future.- 5.1 Introduction.- 5.2 The T800 family.- 5.3 The T9000 family.- 5.4 The Chameleon family.- 6 Adaptor: A Compilation System for Data Parallel Fortran Programs.- 6.1 Introduction.- 6.2 The Adaptor Compilation System.- 6.2.1 Properties of Adaptor.- 6.2.2 Overview of Adaptor.- 6.2.3 The Input Language.- 6.2.4 Programming Models for the Generated Programs.- 6.2.5 Interactive Source-to-Source Transformation.- 6.2.6 Realization of the Translation.- 6.2.7 Distributed Array Library.- 6.2.8 Visualization of the Run Time Behavior.- 6.2.9 Availability.- 6.2.10 Related Work.- 6.3 Results of Benchmark Codes.- 6.3.1 The Purdue Set.- 6.3.2 Comparison of Sequential and Parallel Version.- 6.3.3 Efficiency and Scalability.- 6.3.4 Adaptor vs. hand-coded message passing programs.- 6.3.5 Full vs. Loosely Synchronous Execution.- 6.4 Results of Application Codes.- 6.4.1 HYDFLO: a CM Fortran Code for Fluid Dynamics.- 6.4.2 ESM: a Fortran 90 Code for Circulation.- 6.4.3 IFS: a Fortran 77 Code for Weather Prediction.- 6.5 Summary.- 7 SNAP! Prototyping a Sequential and Numerical Application Parallelizer.- 7.1 Introduction.- 7.2 Compiler.- 7.2.1 Front-End for FORTRAN.- 7.2.2 Dependence Analysis.- 7.2.3 Alignment analysis.- 7.2.4 Parallelizer.- 7.2.5 Code generation.- 7.3 Conclusions.- 8 Knowledge—Based Automatic Parallelization by Pattern Recognition.- 8.1 Introduction and Overview.- 8.2 Preprocessing the Source Code.- 8.3 Which Patterns are Supported?.- 8.4 Pattern Recognition: A Detailed View.- 8.4.1 Program Representation.- 8.4.2 Pattern Hierarchy Graph.- 8.4.3 The Matching Algorithm.- 8.4.4 Standard Pattern Matching: A simple example.- 8.4.5 Removing redundant IF statements.- 8.4.6 Loop Rerolling.- 8.4.7 Difference Stars.- 8.4.8 Beyond standard matching: Identification of multigrid hierarchies.- 8.5 A Parallel Algorithm for each Pattern.- 8.6 Alignment and Partitioning.- 8.7 Determining Cost Functions: Estimating and Benchmarking.- 8.8 Implementation and Future Extensions.- 8.9 Conclusions.- 9 Automatic Data Layout for Distributed—Memory Machines in the D Programming Environment.- 9.1 Introduction.- 9.2 Compilation system.- 9.3 Dynamic Data Layout: Two Examples.- 9.4 Towards Dynamic Data Layout.- 9.4.1 Alignment Analysis.- 9.4.2 Distribution Analysis.- 9.4.3 Inter-Phase Decomposition Analysis.- 9.5 Related Work.- 9.6 Summary and Future Work.- 10 Subspace Optimizations.- 10.1 Introduction.- 10.1.1 Data Optimization.- 10.1.2 Shapes.- 10.2 Subspaces.- 10.3 Subspace Changes.- 10.3.1 Scalars.- 10.3.2 Control Expressions.- 10.3.3 Array Sections.- 10.3.4 Explicit Dimensions.- 10.3.5 Reductions.- 10.4 Subspace Optimizations.- 10.4.1 Relative Costs.- 10.4.2 Subspace Minimization.- 10.4.3 Subspace Minimization with other Types of Expansion.- 10.4.4 Combining Multiple Expansions.- 10.4.5 Expansion Strength Reduction.- 10.4.6 Expansion Costs.- 10.4.7 Reducing the Computation within Expansions.- 10.5 Subspaces Optimization Compared to Alignment.- 10.6 Summary.- 10.7 Acknowledgments.- 11 Data and Process Alignment in Modula-2*.- 11.1 Introduction.- 11.2 Modula-2*.- 11.2.1 FORALL statement.- 11.2.2 Allocation of array data.- 11.3 Alignment in Modula-2*.- 11.3.1 Data Alignment.- 11.3.2 Process Alignment.- 11.4 Arrangement Graphs and Conflicts.- 11.4.1 Type and Structure.- 11.4.2 Conflicts.- 11.5 Cost Considerations.- 11.6 Example.- 11.7 Conclusion.- 12 Automatic Parallelization for Distributed Memory Multiprocessors.- 12.1 Introduction.- 12.2 Related Work.- 12.3 Overview.- 12.4 Parallelization Strategy.- 12.5 Branch—and—Bound Algorithm.- 12.5.1 Basic Approach.- 12.5.2 Distribution Graph.- 12.5.3 Redistribution during Program Execution.- 12.6 Performance Estimator.- 12.6.1 Transfer costs.- 12.6.2 Combining the transfer costs.- 12.6.3 Data Transfer Graph.- 12.7 Prototype Implementation and Results.- 12.7.1 Implementation.- 12.7.2 Livermore Loops.- 12.7.3 Gauss—Seidel Relaxation.- 12.7.4 Jacobi Relaxation.- 12.8 Conclusions and Further Research.- 12.9 Acknowledgements.- A Trademarks.