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PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil mapping
Zhang, Guiming1; Zhu, A-Xing2,3,4,5; Liu, Jing6; Guo, Shanxin7; Zhu, Yunqiang5
2021-02-16
Source PublicationTRANSACTIONS IN GIS
ISSN1361-1682
Pages23
Corresponding AuthorZhang, Guiming(guiming.zhang@du.edu)
AbstractDigital soil mapping (DSM) at high spatial resolutions over large areas often demands considerable computing power. This study aims to harness the heterogeneous computing resources on multi-core central processing units (CPUs) and graphics processing units (GPUs) to accelerate DSM by implementing PyCLiPSM, a parallel version of the iPSM (individual predictive soil mapping) algorithm which represents the type of geospatial algorithms that is data- and compute-intensive and highly parallelizable. PyCLiPSM was implemented in Python based on the PyOpenCL parallel programming library, which runs on any operating system and exploits the computing power of both CPUs and GPUs. Experiments show that PyCLiPSM can effectively leverage multi-core CPUs and GPUs to speed up DSM tasks. PyCLiPSM is open-source and freely available. Using PyCLiPSM as an example, we advocate implementing parallel geospatial algorithms using the PyOpenCL framework to harness the heterogeneous computing resources available to researchers and practitioners for accelerated geospatial analysis.
DOI10.1111/tgis.12730
Indexed BySCI
Language英语
Funding ProjectUniversity of Denver ; National Natural Science Foundation of China[41601212] ; National Natural Science Foundation of China[41871300]
Funding OrganizationUniversity of Denver ; National Natural Science Foundation of China
WOS Research AreaGeography
WOS SubjectGeography
WOS IDWOS:000618617500001
PublisherWILEY
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/160638
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhang, Guiming
Affiliation1.Univ Denver, Dept Geog & Environm, Denver, CO USA
2.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China
4.Nanjing Normal Univ, Sch Geog, Nanjing, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
6.Santa Monica Coll, Earth Sci Dept, Santa Monica, CA USA
7.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr Geospatial Informat, Shenzhen, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Guiming,Zhu, A-Xing,Liu, Jing,et al. PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil mapping[J]. TRANSACTIONS IN GIS,2021:23.
APA Zhang, Guiming,Zhu, A-Xing,Liu, Jing,Guo, Shanxin,&Zhu, Yunqiang.(2021).PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil mapping.TRANSACTIONS IN GIS,23.
MLA Zhang, Guiming,et al."PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil mapping".TRANSACTIONS IN GIS (2021):23.
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