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CPU-GPU hybrid computing for feature extraction from video stream

Authors
Lee, SungjuKim, HeegonPark, DaiheeChung, YongwhaJeong, Taikyeong
Issue Date
2014
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Keywords
CPU; GPU; heterogeneous computing; feature extraction
Citation
IEICE ELECTRONICS EXPRESS, v.11, no.22
Indexed
SCIE
SCOPUS
Journal Title
IEICE ELECTRONICS EXPRESS
Volume
11
Number
22
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/101133
DOI
10.1587/elex.11.20140932
ISSN
1349-2543
Abstract
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.
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College of Science and Technology > Department of Computer Convergence Software > 1. Journal Articles
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과학기술대학 (컴퓨터융합소프트웨어학과)
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