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A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data
Zhang, Guiming1; Zhu, A-Xing1,2,3,4,5; Huang, Qunying1
2017
Source PublicationINTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
ISSN1365-8816
Volume31Issue:10Pages:2068-2097
Corresponding AuthorZhu, A-Xing(azhu@wisc.edu)
AbstractKernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm lever-aging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data.
KeywordAdaptive kernel density estimation optimization GPU/CUDA OpenMP spatial big data
DOI10.1080/13658816.2017.1324975
WOS KeywordBANDWIDTH ; CYBERINFRASTRUCTURE ; GEOCOMPUTATION ; ALGORITHM ; SELECTION ; SCIENCES ; NETWORK ; RANGE ; MODEL ; CUDA
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41431177] ; National Basic Research Program of China[2015CB954102] ; Natural Science Research Program of Jiangsu[14KJA170001] ; PAPD ; National Key Technology Innovation Project for Water Pollution Control and Remediation[2013ZX07103006] ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison ; 'One-Thousand Talents' Program of China
Funding OrganizationNational Natural Science Foundation of China ; National Basic Research Program of China ; Natural Science Research Program of Jiangsu ; PAPD ; National Key Technology Innovation Project for Water Pollution Control and Remediation ; Vilas Associate Award ; Hammel Faculty Fellow Award ; Manasse Chair Professorship from the University of Wisconsin-Madison ; 'One-Thousand Talents' Program of China
WOS Research AreaComputer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS SubjectComputer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science
WOS IDWOS:000405678300008
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62783
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhu, A-Xing
Affiliation1.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
2.Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing, Jiangsu, Peoples R China
3.State Key Lab Cultivat Base Geog Environm Evolut, Nanjing, Jiangsu, Peoples R China
4.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
5.Jiangsu Ctr Collaborat Innovat Geog Informat Res, Nanjing, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Guiming,Zhu, A-Xing,Huang, Qunying. A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2017,31(10):2068-2097.
APA Zhang, Guiming,Zhu, A-Xing,&Huang, Qunying.(2017).A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,31(10),2068-2097.
MLA Zhang, Guiming,et al."A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 31.10(2017):2068-2097.
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