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

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 9th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2011
ページ135-142
ページ数8
DOI
出版ステータスPublished - 2011 8 18
イベント9th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2011 - Busan, Korea, Republic of
継続期間: 2011 5 262011 5 28

出版物シリーズ

名前Proceedings - 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
国/地域Korea, Republic of
CityBusan
Period11/5/2611/5/28

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用

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