Automatic Tuning Of Compilers Using Machine Learning (Springerbriefs In Applied Sciences And Technology)

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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 comprehensiv...

Series: SpringerBriefs in Applied Sciences and Technology
Paperback: 118 pages
Publisher: Springer; 1st ed. 2018 edition (December 22, 2017)
Language: English
ISBN-10: 9783319714882
ISBN-13: 978-3319714882
ASIN: 3319714880
Product Dimensions: 6.1 x 0.3 x 9.2 inches
Amazon Rank: 2698338
Format: PDF ePub Text TXT fb2 ebook

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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.