Benchmarking Parallel K-Means Cloud Type Clustering from Satellite Data
| dc.contributor.author | Barajas, Carlos | |
| dc.contributor.author | Guo, Pei | |
| dc.contributor.author | Mukherjee, Lipi | |
| dc.contributor.author | Hoban, Susan | |
| dc.contributor.author | Wang, Jianwu | |
| dc.contributor.author | Jin, Daeho | |
| dc.contributor.author | Gangopadhyay, Aryya | |
| dc.contributor.author | Gobbert, Matthias K. | |
| dc.date.accessioned | 2019-12-19T14:33:06Z | |
| dc.date.available | 2019-12-19T14:33:06Z | |
| dc.date.issued | 2019-10-08 | |
| 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. 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.sponsorship | This 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.uri | https://link.springer.com/chapter/10.1007/978-3-030-32813-9_20#citeas | en_US |
| dc.format.extent | 27 pages | en_US |
| dc.genre | books chapters | en_US |
| dc.identifier | doi:10.13016/m2uojh-skfj | |
| dc.identifier.citation | Barajas, 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#citeas | en_US |
| dc.identifier.uri | https://doi.org/10.1007/978-3-030-32813-9_20 | |
| dc.identifier.uri | http://hdl.handle.net/11603/16915 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Springer, Cham | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Mathematics Department Collection | |
| dc.relation.ispartof | UMBC Information Systems Department | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC Joint Center for Earth Systems Technology (JCET) | |
| dc.relation.ispartof | UMBC Physics Department | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| 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 | parallel computing | en_US |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | en_US |
| dc.subject | MPI | en_US |
| dc.subject | OpenMP | en_US |
| dc.subject | Spark | en_US |
| dc.subject | K-means clustering | en_US |
| dc.title | Benchmarking Parallel K-Means Cloud Type Clustering from Satellite Data | en_US |
| dc.type | Text | en_US |