Use of Code Structural Features for Machine Learning to Predict Effective Optimizations

Yuki Kawarabatake, Mulya Agung, Kazuhiko Komatsu, Ryusuke Egawa, Hiroyuki Takizawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Since it is difficult to explicitly express the underlying relationship between a code and appropriate optimizations for it, this paper discusses a possibility of using machine learning to predict appropriate optimizations for a given code. In this paper, selection of appropriate compiler options is taken as an example of the prediction, because it can be seen as selection of code optimizations; use of different compiler options results in enabling different compiler optimizations. One severe problem is that it is difficult to collect a sufficient number of data for a machine learning model to well understand the underlying relationships among codes and their appropriate optimizations. Therefore, in addition to conventional features of a code, such as profiling data and parameterized code features, we directly use a code structure itself to retrieve more information from a limited number of codes. The evaluation results suggest that use of code structural features can potentially improve the prediction accuracy if the training dataset contains data of similar code structures.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1049-1055
Number of pages7
ISBN (Print)9781538655559
DOIs
Publication statusPublished - 2018 Aug 3
Event32nd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018 - Vancouver, Canada
Duration: 2018 May 212018 May 25

Publication series

NameProceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018

Other

Other32nd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018
CountryCanada
CityVancouver
Period18/5/2118/5/25

Keywords

  • Code structural features
  • Machine learning
  • Performance optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management

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