CPU-GPU hybrid computing for feature extraction from video stream
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sungju | - |
dc.contributor.author | Kim, Heegon | - |
dc.contributor.author | Park, Daihee | - |
dc.contributor.author | Chung, Yongwha | - |
dc.contributor.author | Jeong, Taikyeong | - |
dc.date.accessioned | 2021-09-05T17:13:28Z | - |
dc.date.available | 2021-09-05T17:13:28Z | - |
dc.date.created | 2021-06-15 | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 1349-2543 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/101133 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG | - |
dc.title | CPU-GPU hybrid computing for feature extraction from video stream | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Sungju | - |
dc.contributor.affiliatedAuthor | Park, Daihee | - |
dc.contributor.affiliatedAuthor | Chung, Yongwha | - |
dc.identifier.doi | 10.1587/elex.11.20140932 | - |
dc.identifier.scopusid | 2-s2.0-84911916905 | - |
dc.identifier.wosid | 000346400600006 | - |
dc.identifier.bibliographicCitation | IEICE ELECTRONICS EXPRESS, v.11, no.22 | - |
dc.relation.isPartOf | IEICE ELECTRONICS EXPRESS | - |
dc.citation.title | IEICE ELECTRONICS EXPRESS | - |
dc.citation.volume | 11 | - |
dc.citation.number | 22 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordAuthor | CPU | - |
dc.subject.keywordAuthor | GPU | - |
dc.subject.keywordAuthor | heterogeneous computing | - |
dc.subject.keywordAuthor | feature extraction | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.