Benchmarking Parallel K-Means Cloud Type Clustering from Satellite Data

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dc.contributor.authorBarajas, Carlos
dc.contributor.authorGuo, Pei
dc.contributor.authorMukherjee, Lipi
dc.contributor.authorHoban, Susan
dc.contributor.authorWang, Jianwu
dc.contributor.authorJin, Daeho
dc.contributor.authorGangopadhyay, Aryya
dc.contributor.authorGobbert, Matthias K.
dc.date.accessioned2019-12-19T14:33:06Z
dc.date.available2019-12-19T14:33:06Z
dc.date.issued2019-10-08
dc.description.abstractThe study of clouds, i.e., where they occur and what are their characteristics, plays a key role in the understanding of climate change. Clustering is a common machine learning technique used in atmospheric science to classify cloud types. Many parallelism techniques e.g., MPI, OpenMP and Spark, could achieve efficient and scalable clustering of large-scale satellite observation data. In order to understand their differences, this paper studies and compares three different approaches on parallel clustering of satellite observation data. Benchmarking experiments with k-means clustering are conducted with three parallelism techniques, namely OpenMP, OpenMP+MPI, and Spark, on a HPC cluster using up to 16 nodes.en_US
dc.description.sponsorshipThis work is supported by NSF grant with number OAC–1730250 and NASA grant 80NSSC17K0366. The hardware used the UMBC High Performance Computing Facility, which is supported by NSF grants (CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (DMS–0821311), with additional substantial support from UMBC.en_US
dc.description.urihttps://link.springer.com/chapter/10.1007/978-3-030-32813-9_20#citeasen_US
dc.format.extent27 pagesen_US
dc.genrebooks chaptersen_US
dc.identifierdoi:10.13016/m2uojh-skfj
dc.identifier.citationBarajas, Carlos; Guo, Pei; Mukherjee, Lipi; Hoban, Susan; Wang, Jianwu; Jin, Daeho; Gangopadhyay, Aryya; Gobbert, Matthias K.; Benchmarking Parallel K-Means Cloud Type Clustering from Satellite Data; International Symposium on Benchmarking, Measuring and Optimization Journal; Benchmarking, Measuring, and Optimizing pp 248-260; https://link.springer.com/chapter/10.1007/978-3-030-32813-9_20#citeasen_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-32813-9_20
dc.identifier.urihttp://hdl.handle.net/11603/16915
dc.language.isoen_USen_US
dc.publisherSpringer, Chamen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Physics Department
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsPublic Domain Mark 1.0*
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.subjectparallel computingen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.subjectMPIen_US
dc.subjectOpenMPen_US
dc.subjectSparken_US
dc.subjectK-means clusteringen_US
dc.titleBenchmarking Parallel K-Means Cloud Type Clustering from Satellite Dataen_US
dc.typeTexten_US

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