CPU-GPU hybrid computing for feature extraction from video stream

被引：5
|
作者：
Lee, Sungju [1 ]
Kim, Heegon [1 ]
Park, Daihee [1 ]
Chung, Yongwha [1 ]
Jeong, Taikyeong [2 ]
机构：
[1] Korea Univ, Dept Comp & Informat Sci, Sejong, South Korea
[2] Seoul Womens Univ, Dept Comp Sci & Engn, Seoul, South Korea
来源：
IEICE ELECTRONICS EXPRESS | 2014年 / 11卷 / 22期
关键词：
CPU; GPU; heterogeneous computing; feature extraction;
D O I：
10.1587/elex.11.20140932
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页数：8
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