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Automatic Tuning of Compilers Using Machine Learning: SpringerBriefs in Applied Sciences and Technology

Autor Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano
en Limba Engleză Paperback – 19 ian 2018
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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Specificații

ISBN-13: 9783319714882
ISBN-10: 3319714880
Pagini: 121
Ilustrații: XVII, 118 p. 23 illus., 6 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.2 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seriile SpringerBriefs in Applied Sciences and Technology, PoliMI SpringerBriefs

Locul publicării:Cham, Switzerland

Cuprins

Background.- DSE Approach for Compiler Passes.- Addressing the Selection Problem of Passes using ML.- Intermediate Speedup Prediction for the Phase-ordering Problem.- Full-sequence Speedup Prediction for the Phase-ordering Problem.- Concluding Remarks. 

Textul de pe ultima copertă

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.

Caracteristici

Includes supplementary material: sn.pub/extras