A history-based performance prediction model with profile data classification for automatic task allocation in heterogeneous computing systems

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

8 Citations (Scopus)

Abstract

In this paper, we propose a runtime performance prediction model for automatic selection of accelerators to execute kernels in OpenCL. The proposed method is a history-based approach that uses profile data for performance prediction. The profile data are classified into some groups, from each of which its own performance model is derived. As the execution time of a kernel depends on some runtime parameters such as kernel arguments, the proposed method first identifies parameters affecting the execution time by calculating the correlation between each parameter and the execution time. A parameter with weak correlation is used for the classification of the profile data and the selection of the performance prediction model. A parameter with strong correlation is used for building a linear model for the prediction of the kernel execution time by using only the classified profile data. Experimental results clearly indicate that the proposed method can achieve more accurate performance prediction than conventional history-based approaches because of the profile data classification.

Original languageEnglish
Title of host publicationProceedings - 9th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2011
Pages135-142
Number of pages8
DOIs
Publication statusPublished - 2011 Aug 18
Event9th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2011 - Busan, Korea, Republic of
Duration: 2011 May 262011 May 28

Publication series

NameProceedings - 9th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2011

Other

Other9th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2011
Country/TerritoryKorea, Republic of
CityBusan
Period11/5/2611/5/28

Keywords

  • GPGPU
  • Heterogeneous
  • History-based
  • OpenCL
  • Performance prediction

ASJC Scopus subject areas

  • Computer Science Applications

Fingerprint

Dive into the research topics of 'A history-based performance prediction model with profile data classification for automatic task allocation in heterogeneous computing systems'. Together they form a unique fingerprint.

Cite this