Benchmarking parallel implementations of cloud type clustering from satellite data

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dc.contributor.authorBarajas, Carlos A.
dc.contributor.authorMukherjee, Lipi
dc.contributor.authorGuo, Pei
dc.contributor.authorHoban, Susan
dc.contributor.authorJin, Daeho
dc.contributor.authorGangopadhyay, Aryya
dc.contributor.authorWang, Jianwu
dc.date.accessioned2020-07-29T17:30:20Z
dc.date.available2020-07-29T17:30:20Z
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. The aim of this project is to use machine learning in conjunction with parallel computing techniques to classify cloud types. Experiments with k-means clustering are conducted with two parallelism techniques.en_US
dc.description.sponsorshipThis work is supported by the grant CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources from the National Science Foundation (grant no. OAC–1730250). Co-author Lipi Mukherjee additionally acknowledges the NASA Earth and Space Science Fellowship Program - supported by NASA Headquarters. The hardware in the UMBC High Performance Computing Facility (HPCF) is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources.en_US
dc.description.urihttp://hpcf-files.umbc.edu/research/papers/CT2018Team2.pdfen_US
dc.format.extent13 pagesen_US
dc.genretechnical reportsen_US
dc.identifierdoi:10.13016/m2jtl3-hmjx
dc.identifier.citationCarlos Barajas et al., Benchmarking parallel implementations of cloud type clustering from satellite data, http://hpcf-files.umbc.edu/research/papers/CT2018Team2.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/19273
dc.language.isoen_USen_US
dc.publisherUMBCen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Physics Department
dc.relation.ispartofseriesHPCF–2018–11;
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.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleBenchmarking parallel implementations of cloud type clustering from satellite dataen_US
dc.typeTexten_US

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