Benchmarking Parallel K-Means Cloud Type Clustering from Satellite Data

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2019_Book_BenchmarkingMeasuringAndOptimi.pdf (1.93 MB)

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https://link.springer.com/chapter/10.1007/978-3-030-32813-9_20#citeas

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https://doi.org/10.1007/978-3-030-32813-9_20
 http://hdl.handle.net/11603/16915

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UMBC Mathematics and Statistics Department
UMBC Faculty Collection
UMBC Information Systems Department
UMBC Joint Center for Earth Systems Technology (JCET)
UMBC Physics Department
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Date

2019-10-08

Type of Work

books chapters
Text

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Citation of Original Publication

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

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Public Domain Mark 1.0
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.

Subjects

parallel computing
UMBC High Performance Computing Facility (HPCF)
MPI
OpenMP
Spark
K-means clustering

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.