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Automatic tuning of compilers using ...

Ashouri, Amir H.

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Automatic tuning of compilers using machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automatic tuning of compilers using machine learning/ by Amir H. Ashouri ... [et al.].
other author:	Ashouri, Amir H.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xvii, 118 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Compilers (Computer programs) -
Online resource:	http://dx.doi.org/10.1007/978-3-319-71489-9
ISBN:	9783319714899

Automatic tuning of compilers using machine learning
Automatic tuning of compilers using machine learning[electronic resource] /by Amir H. Ashouri ... [et al.]. - Cham :Springer International Publishing :2018. - xvii, 118 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

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.

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.

ISBN: 9783319714899

Standard No.: 10.1007/978-3-319-71489-9doiSubjects--Topical Terms:

535138
Compilers (Computer programs)

LC Class. No.: QA76.76.C65

Dewey Class. No.: 005.453

Automatic tuning of compilers using machine learning
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245 0 0 $a Automatic tuning of compilers using machine learning $h [electronic resource] / $c by Amir H. Ashouri ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xvii, 118 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology, $x 2191-530X
505 0 $a 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.
520 $a 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.
650 0 $a Compilers (Computer programs) $3 535138
650 0 $a Machine learning. $3 533906
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 891123
650 2 4 $a Simulation and Modeling. $3 890873
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
700 1 $a Ashouri, Amir H. $3 3299757
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in applied sciences and technology. $3 1565541
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-71489-9
950 $a Engineering (Springer-11647)