An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data

	An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data
	Liu, Jia 1; Feld, Dustin 1; Xue, Yong 1; Garcke, Jochen 1; Soddemann, Thomas 1; Pan, Peiyuan 1
刊名	INTERNATIONAL JOURNAL OF DIGITAL EARTH
	2016
卷号	9 期号:8 页码:748-765
关键词	LEAF-AREA INDEX CHLOROPHYLL CONTENT CANOPY REFLECTANCE VEGETATION INDEX CLASSIFICATION VALIDATION INVERSION VARIABLES PROSPECT WALKING
通讯作者	Xue, Y (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China. ; Xue, Y (reprint author), London Metropolitan Univ, Fac Life Sci & Comp, London, England.
英文摘要	Quantitative remote sensing retrieval algorithms help understanding the dynamic aspects of Digital Earth. However, the Big Data and complex models in Digital Earth pose grand challenges for computation infrastructures. In this article, taking the aerosol optical depth (AOD) retrieval as a study case, we exploit parallel computing methods for high efficient geophysical parameter retrieval. We present an efficient geocomputation workflow for the AOD calculation from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. According to their individual potential for parallelization, several procedures were adapted and implemented for a successful parallel execution on multi-core processors and Graphics Processing Units (GPUs). The benchmarks in this paper validate the high parallel performance of the retrieval workflow with speedups of up to 5.x on a multi-core processor with 8 threads and 43.x on a GPU. To specifically address the time-consuming model retrieval part, hybrid parallel patterns which combine the multi-core processor's and the GPU's compute power were implemented with static and dynamic workload distributions and evaluated on two systems with different CPU-GPU configurations. It is shown that only the dynamic hybrid implementation leads to a greatly enhanced overall exploitation of the heterogeneous hardware environment in varying circumstances.
学科主题	Physical Geography; Remote Sensing
类目[WOS]	Geography, Physical ; Remote Sensing
收录类别	SCI
语种	英语
WOS记录号	WOS:000382198900002
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39256]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China 2.London Metropolitan Univ, Fac Life Sci & Comp, London, England 3.Fraunhofer Inst Algorithms & Sci Comp SCAI, St Augustin, Germany 4.Univ Bonn, Inst Numer Simulat, Bonn, Germany 5.Univ Chinese Acad Sci, Beijing, Peoples R China 6.Univ Cologne, Dept Comp Sci, Cologne, Germany
推荐引用方式 GB/T 7714	Liu, Jia,Feld, Dustin,Xue, Yong,et al. An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2016,9(8):748-765.
APA	Liu, Jia,Feld, Dustin,Xue, Yong,Garcke, Jochen,Soddemann, Thomas,&Pan, Peiyuan.(2016).An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data.INTERNATIONAL JOURNAL OF DIGITAL EARTH,9(8),748-765.
MLA	Liu, Jia,et al."An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data".INTERNATIONAL JOURNAL OF DIGITAL EARTH 9.8(2016):748-765.