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Automatic Tuning of Compilers Using Machine Learning

  • Book
  • © 2018

Overview

Authors:
  1. Amir H. Ashouri

    1. Edward S. Rogers Sr. Department of Electrical and Computer Engineering (ECE), University of Toronto, Toronto, Canada

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  2. Gianluca Palermo

    1. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy

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  3. John Cavazos

    1. Computer and Information Sciences (CIS), University of Delaware, Newark, USA

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  4. Cristina Silvano

    1. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy

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Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

Part of the book sub series: PoliMI SpringerBriefs (BRIEFSPOLIMI)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Background

    • Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano
    Pages 1-22

  3. Design Space Exploration of Compiler Passes: A Co-Exploration Approach for the Embedded Domain

    • Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano
    Pages 23-39

  4. Selecting the Best Compiler Optimizations: A Bayesian Network Approach

    • Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano
    Pages 41-70

  5. The Phase-Ordering Problem: An Intermediate Speedup Prediction Approach

    • Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano
    Pages 71-83

  6. The Phase-Ordering Problem: A Complete Sequence Prediction Approach

    • Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano
    Pages 85-113

  7. Concluding Remarks

    • Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano
    Pages 115-116

  8. Back Matter

    Pages 117-118
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Keywords

About this book

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.

Authors and Affiliations

  • Edward S. Rogers Sr. Department of Electrical and Computer Engineering (ECE), University of Toronto, Toronto, Canada

    Amir H. Ashouri

  • Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy

    Gianluca Palermo, Cristina Silvano

  • Computer and Information Sciences (CIS), University of Delaware, Newark, USA

    John Cavazos

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