Automatic Tuning of Compilers Using Machine Learning

Amir H. Ashouri

ISBN 10: 3319714880 / ISBN 13: 9783319714882

Published by Springer, Berlin, Springer International Publishing, Springer, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783319714882

Bibliographic Details

• Title: Automatic Tuning of Compilers Using Machine Learning
• Publisher: Springer, Berlin, Springer International Publishing, Springer
• Publication Date: 2018
• Language: English
• ISBN 10: 3319714880
• ISBN 13: 9783319714882
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 236W x 155H x 9D millimeters 236 millimeters width by 155 millimeters height by 9 millimeters depth
• Weight: 230 grams

About this title

Synopsis

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.