Automatic Parallelization

New Approaches to Code Generation, Data Distribution, and Performance Prediction

Häftad, Engelska, 1994

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Beskrivning

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?

Produktinformation

Utgivningsdatum:1994-01-01
Mått:148 x 210 x 14 mm
Vikt:316 g
Format:Häftad
Språk:Engelska
Antal sidor:224
Upplaga:1994
Förlag:Friedrich Vieweg & Sohn Verlagsgesellschaft mbH
ISBN:9783528054014

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Innehållsförteckning

2 The Weight Finder — An Advanced Profiler for Fortran Programs.- 2.1 Introduction.- 2.2 Prerequisite.- 2.3 The Weight Finder.- 2.4 Adaptation of Profile Data.- 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.- 3.5 Discussion and Further Work.- 4 Isolating the Reasons for the Performance of Parallel Machines on Numerical Programs.- 4.1 Introduction.- 4.2 Micro Measurements.- 4.3 Measurements.- 4.4 Algorithms.- 4.5 Analysis of the Programs.- 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.- 6.3 Results of Benchmark Codes.- 6.4 Results of Application Codes.- 6.5 Summary.- 7 SNAP! Prototyping a Sequential and Numerical Application Parallelizer.- 7.1 Introduction.- 7.2 Compiler.- 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.- 8.5 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.- 9.5 Related Work.- 9.6 Summary and Future Work.- 10 SubspaceOptimizations.- 10.1 Introduction.- 10.2 Subspaces.- 10.3 Subspace Changes.- 10.4 Subspace Optimizations.- 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*.- 11.3 Alignment in Modula-2*.- 11.4 Arrangement Graphs and 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.- 12.6 Performance Estimator.- 12.7 Prototype Implementation and Results.- 12.8 Conclusions and Further Research.- 12.9 Acknowledgements.- A Trademarks.