High-Performance Time-Series Quantitative Retrieval From Satellite Images on a GPU Cluster

doi:10.1109/JSTARS.2019.2920077

CUG-IR

	High-Performance Time-Series Quantitative Retrieval From Satellite Images on a GPU Cluster
	Liu Jia 1,2; Xue Yong 3,4; Ren Kaijun 2; Song Junqiang 2; Windmill Christopher 5; Merritt Patrick 5
	2019-08
发表期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷号	12 期号:8 页码:2821-2832
原始文献类型	Journal article (JA)
摘要	The quality and accuracy of remote sensing instruments continue to increase, allowing geoscientists to perform various quantitative retrieval applications to observe the geophysical variables of land, atmosphere, ocean, etc. The explosive growth of time-series remote sensing (RS) data over large-scales poses great challenges on managing, processing, and interpreting RS "Big Data." To explore these time-series RS data efficiently, in this paper, we design and implement a high-performance framework to address the time-consuming time-series quantitative retrieval issue on a graphics processing unit cluster, taking the aerosol optical depth (AOD) retrieval from satellite images as a study case. The presented framework exploits the multilevel parallelism for time-series quantitative RS retrieval to promote efficiency. At the coarse-grained level of parallelism, the AOD time-series retrieval is represented as multidirected acyclic graph workflows and scheduled based on a list-based heuristic algorithm, heterogeneous earliest finish time, taking the idle slot and priorities of retrieval jobs into account. At the fine-grained level, the parallel strategies for the major remote sensing image processing algorithms divided into three categories, i.e., the point or pixel-based operations, the local operations, and the global or irregular operations have been summarized. The parallel framework was implemented with message passing interface and compute unified device architecture, and experimental results with the AOD retrieval case verify the effectiveness of the presented framework.
关键词	Aerosol optical depth (AOD) graphics processing unit (GPU) cluster high-performance computing multidirected acyclic graph (DAG) scheduling multilevel parallelism quantitative remote sensing retrieval time series
DOI	10.1109/JSTARS.2019.2920077
语种	英语
ISSN	1939-1404
收录类别	SCI ; EI
EI入藏号	20193907478058
WOS记录号	WOS:000487530100020
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：31[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.cug.edu.cn/handle/2XU834YA/87824
专题	中国地质大学（武汉）
作者单位	1.China Univ Geosci, Sch Comp Sci, Wuhan 430074, Hubei, Peoples R China; 2.Natl Univ Def Technol, Coll Meteorol & Oceanog, Changsha 410073, Hunan, Peoples R China; 3.Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China; 4.Univ Derby, Dept Elect Comp & Math, Coll Engn & Technol, Derby DE22 1GB, England; 5.Univ Derby, Coll Engn & Technol, Dept Elect Comp & Math, Derby DE22 1GB, England
推荐引用方式 GB/T 7714	Liu Jia,Xue Yong,Ren Kaijun,et al. High-Performance Time-Series Quantitative Retrieval From Satellite Images on a GPU Cluster[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2019,12(8):2821-2832.
APA	Liu Jia,Xue Yong,Ren Kaijun,Song Junqiang,Windmill Christopher,&Merritt Patrick.(2019).High-Performance Time-Series Quantitative Retrieval From Satellite Images on a GPU Cluster.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,12(8),2821-2832.
MLA	Liu Jia,et al."High-Performance Time-Series Quantitative Retrieval From Satellite Images on a GPU Cluster".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 12.8(2019):2821-2832.

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