Benchmarking parallel implementations of cloud type clustering from satellite data
| dc.contributor.author | Barajas, Carlos A. | |
| dc.contributor.author | Mukherjee, Lipi | |
| dc.contributor.author | Guo, Pei | |
| dc.contributor.author | Hoban, Susan | |
| dc.contributor.author | Jin, Daeho | |
| dc.contributor.author | Gangopadhyay, Aryya | |
| dc.contributor.author | Wang, Jianwu | |
| dc.date.accessioned | 2020-07-29T17:30:20Z | |
| dc.date.available | 2020-07-29T17:30:20Z | |
| dc.description.abstract | The 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.sponsorship | This 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.uri | http://hpcf-files.umbc.edu/research/papers/CT2018Team2.pdf | en_US |
| dc.format.extent | 13 pages | en_US |
| dc.genre | technical reports | en_US |
| dc.identifier | doi:10.13016/m2jtl3-hmjx | |
| dc.identifier.citation | Carlos Barajas et al., Benchmarking parallel implementations of cloud type clustering from satellite data, http://hpcf-files.umbc.edu/research/papers/CT2018Team2.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/19273 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | UMBC | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Mathematics Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC Information Systems Department | |
| dc.relation.ispartof | UMBC Physics Department | |
| dc.relation.ispartofseries | HPCF–2018–11; | |
| dc.rights | This 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.rights | Public Domain Mark 1.0 | * |
| dc.rights | This 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.uri | http://creativecommons.org/publicdomain/mark/1.0/ | * |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | en_US |
| dc.title | Benchmarking parallel implementations of cloud type clustering from satellite data | en_US |
| dc.type | Text | en_US |