A Processor Selection Method based on Execution Time Estimation for Machine Learning Programs

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

Abstract

In recent years, machine learning has become widespread. Since machine learning algorithms have become complex and the amount of data to be handled have become large, the execution times of machine learning programs have been increasing. Processors called accelerators can contribute to the execution of a machine learning program with a short time. However, the processors including the accelerators have different characteristics. Therefore, it is unclear whether existing machine learning programs are executed on the appropriate processor or not. This paper proposes a method for selecting a processor suitable for each machine learning program. In the proposed method, the selection is based on the estimation of the execution time of machine learning programs on each processor. The proposed method does not need to execute a target machine learning program in advance. From the experimental results, it is clarified that the proposed method can achieve up to 5.3 times faster execution than the original implementation by NumPy. These results prove that the proposed method can be used in a system that automatically selects the processor so that each machine learning program can be easily executed on the best processor.

Original languageEnglish
Title of host publication2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages779-788
Number of pages10
ISBN (Electronic)9781665435772
DOIs
Publication statusPublished - 2021 Jun
Event2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - Virtual, Portland, United States
Duration: 2021 May 17 → …

Publication series

Name2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021

Conference

Conference2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021
Country/TerritoryUnited States
CityVirtual, Portland
Period21/5/17 → …

Keywords

  • Accelerator
  • Automatic tuning
  • GPU
  • Performance modeling
  • Processor selection
  • Vector processor

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems

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