Automatic Tuning of Compilers Using Machine Learning

1. Auflage 2018
ISBN: 978-3-319-71488-2
Verlag: Springer

Buch, Englisch, 118 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 297 g

Reihe: SpringerBriefs in Applied Sciences and Technology

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.

Zielgruppe

Research

Autoren/Hrsg.

Ashouri, Amir H.

Palermo, Gianluca

Cavazos, John

Silvano, Cristina

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Compiler
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Rechnerarchitektur

Inhaltsverzeichnis

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.