Benchmarking parallel implementations of cloud type clustering from satellite data
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Carlos Barajas et al., Benchmarking parallel implementations of cloud type clustering from satellite data, http://hpcf-files.umbc.edu/research/papers/CT2018Team2.pdf
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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.
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.
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.