Automatic Tuning of Compilers Using Machine Learning

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,...

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Bibliographic Details
Main Authors: Ashouri, Amir H. (Author), Palermo, Gianluca (Author), Cavazos, John (Author), Silvano, Cristina (Author)
Corporate Author: SpringerLink (Online service)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
Edition:1st ed. 2018.
Series:PoliMI SpringerBriefs,
Subjects:
Computational intelligence.
Compilers (Computer programs).
Computer simulation.
Artificial intelligence.
Computational Intelligence.
Compilers and Interpreters.
Computer Modelling.
Artificial Intelligence.
Online Access:Full-text access
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Table of Contents:
  • 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. .

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