CPU-GPU hybrid computing for feature extraction from video stream

IEICE Electronics Express

Daihee Park¹  Sungju Lee¹  Heegon Kim¹  Yongwha Chung¹  Taikyeong Jeong² 

[1] Dept. of Computer and Information Science, Korea University;Dept. of Computer Science and Engineering, Seoul Women’s University

关键词: CPU;    GPU;    heterogeneous computing;    feature extraction;   

DOI  :  10.1587/elex.11.20140932

学科分类：电子、光学、磁材料

来源: Denshi Jouhou Tsuushin Gakkai

【 摘 要 】

References(9)In this paper, we propose a way to distribute the video analytics workload into both the CPU and GPU, with a performance prediction model including characteristics of feature extraction from the video stream data. That is, we estimate the total execution time of a CPU-GPU hybrid computing system with the performance prediction model, and determine the optimal workload ratio and how to use the CPU cores for the given workload. Based on experimental results, we confirm that our proposed method can improve the speedups of three typical workload distributions: CPU-only, GPU-only, or CPU-GPU hybrid computing with a 50:50 workload ratio.

【 授权许可】

Unknown