Automatic Tuning of Compilers Using Machine Learning
Authors: Ashouri, A.H., Palermo, G., Cavazos, J., Silvano, C.
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- About this book
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This book explores break-through approaches to tackling and mitigating the well-known problems of compiler optimization using design space exploration and machine learning techniques. It demonstrates that not all the optimization passes are suitable for use within an optimization sequence and that, in fact, many of the available passes tend to counteract one another. After providing a comprehensive survey of currently available methodologies, including many experimental comparisons with state-of-the-art compiler frameworks, the book describes new approaches to solving the problem of selecting the best compiler optimizations and the phase-ordering problem, allowing readers to overcome the enormous complexity of choosing the right order of optimizations for each code segment in an application. As such, the book offers a valuable resource for a broad readership, including researchers interested in Computer Architecture, Electronic Design Automation and Machine Learning, as well as computer architects and compiler developers.
- Table of contents (6 chapters)
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Background
Pages 1-22
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Design Space Exploration of Compiler Passes: A Co-Exploration Approach for the Embedded Domain
Pages 23-39
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Selecting the Best Compiler Optimizations: A Bayesian Network Approach
Pages 41-70
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The Phase-Ordering Problem: An Intermediate Speedup Prediction Approach
Pages 71-83
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The Phase-Ordering Problem: A Complete Sequence Prediction Approach
Pages 85-113
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Table of contents (6 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Automatic Tuning of Compilers Using Machine Learning
- Authors
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- Amir Hossein Ashouri
- Gianluca Palermo
- John Cavazos
- Cristina Silvano
- Series Title
- PoliMI SpringerBriefs
- Copyright
- 2018
- Publisher
- Springer International Publishing
- Copyright Holder
- The Author(s)
- eBook ISBN
- 978-3-319-71489-9
- DOI
- 10.1007/978-3-319-71489-9
- Softcover ISBN
- 978-3-319-71488-2
- Series ISSN
- 2282-2577
- Edition Number
- 1
- Number of Pages
- XVII, 118
- Number of Illustrations
- 17 b/w illustrations, 6 illustrations in colour
- Topics
