Intelligent Policy Selection for GPU Warp Scheduler

Lih Yih Chiou, Tsung Han Yang, Jian Tang Syu, Che Pin Chang, Yeong Jar Chang

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

Abstract

The graphics processing unit (GPU) is widely used in applications that require massive computing resources such as big data, machine learning, computer vision, etc. As the diversity of applications grows, the GPU's performance becomes difficult to maintain by its warp scheduler. Most of the prior studies of the warp scheduler are based on static analysis of GPU hardware behavior for certain types of benchmarks. We propose for the first time (to the best of our knowledge), a machine learning approach to intelligently select suitable policies for various applications in runtime. The simulation results indicate that the proposed approach can maintain performance comparable to the best policy across different applications.

Original languageEnglish
Title of host publicationProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages302-303
Number of pages2
ISBN (Electronic)9781538678848
DOIs
Publication statusPublished - 2019 Mar
Event1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan
Duration: 2019 Mar 182019 Mar 20

Publication series

NameProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

Conference

Conference1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
Country/TerritoryTaiwan
CityHsinchu
Period19-03-1819-03-20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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