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Parallel Implementation of the Ensemble Empirical Mode Decomposition and Its Application for Earth Science Data Analysis.

Authors :
Shen, Bo-Wen
Cheung, Samson
Wu, Yu-Ling
Li, Jui-Lin F.
Kao, David
Source :
Computing in Science & Engineering; Sep/Oct2017, Vol. 19 Issue 5, p49-57, 9p
Publication Year :
2017

Abstract

To efficiently perform multiscale analysis of high-resolution, global, multiple-dimensional datasets, the authors have deployed the parallel ensemble empirical mode decomposition (PEEMD) package by implementing three-level parallelism into the ensemble empirical mode decomposition (EMD), achieving a scaled performance of 5,000 cores. In this study, they discuss the implementation of the PEEMD and its application for the analysis of Earth science data, including the solution of the Lorenz model, an idealized terrain-induced flow, and Hurricane Sandy. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
15219615
Volume :
19
Issue :
5
Database :
Complementary Index
Journal :
Computing in Science & Engineering
Publication Type :
Academic Journal
Accession number :
125027802
Full Text :
https://doi.org/10.1109/MCSE.2017.3421555