Automatic Tuning of Compilers Using Machine Learning (Paperback, 1st ed. 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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Applied Sciences and Technology
Release date:	2018
First published:	2018
Authors:	Amir H. Ashouri • Gianluca Palermo • John Cavazos • Cristina Silvano
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	118
Edition:	1st ed. 2018
ISBN-13:	978-3-319-71488-2
Categories:	Books > Computing & IT > Applications of computing > Computer modelling & simulation Books > Computing & IT > Computer programming > Programming languages > General Books > Computing & IT > Computer software packages > Computer graphics software > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-319-71488-0
Barcode:	9783319714882

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