Automatic Tuning of Compilers Using Machine Learning

Amir H. Ashouri, Gianluca Palermo, John Cavazos, Cristina Silvano (Autoren)

Buch | Softcover

XVII, 118 Seiten

2018 | 1st ed. 2018
Springer International Publishing (Verlag)
978-3-319-71488-2 (ISBN)

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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 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.

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.

Erscheinungsdatum	01.02.2018
Reihe/Serie	PoliMI SpringerBriefs
Reihe/Serie	SpringerBriefs in Applied Sciences and Technology
Zusatzinfo	XVII, 118 p. 23 illus., 6 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	218 g
Themenwelt	Informatik ► Theorie / Studium ► Compilerbau
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Technik
Schlagworte	Auto-Tuning • Compiler Optimization • Design Space Exploration • Embedded computing • High Performance Computing • performance modeling • Software Characterization
ISBN-10	3-319-71488-0 / 3319714880
ISBN-13	978-3-319-71488-2 / 9783319714882
Zustand	Neuware