Automatic Tuning of Compilers Using Machine Learning

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Programming, Programming Languages, General Computing
Cover of the book Automatic Tuning of Compilers Using Machine Learning by Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano, Springer International Publishing
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Author: Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano ISBN: 9783319714899
Publisher: Springer International Publishing Publication: December 22, 2017
Imprint: Springer Language: English
Author: Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano
ISBN: 9783319714899
Publisher: Springer International Publishing
Publication: December 22, 2017
Imprint: Springer
Language: English

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.

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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.

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