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

To efficiently perform multiscale analysis of high-resolution, global, multiple-dimensional data sets, we have deployed the parallel ensemble empirical mode decomposition (PEEMD) package by implementing three-level parallelism into the ensemble Empirical Mode Decomposition (EMD), achieving a paralle...

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Bibliographic Details
Published in:Computing in science & engineering p. 1
Main Authors: Shen, Bo-Wen, Cheung, Samson, Wu, Yu-ling, Li, Jui-Lin, Kao, David
Format: Journal Article
Language:English
Published: IEEE 08.06.2017
Subjects:
and Visualization
Bridges
Computational modeling
Empirical mode decomposition
G Mathematics of Computing
G.4 Mathematical Software
G.4.a Algorithm design and analysis
G.4.e Parallel and vector implementations
Hurricanes
I Computing Methodologies
I.4 Image Processing and Computer Vision
I.4.10 Image Representation
I.4.10.c Multidimensional
I.6 Simulation
I.6.4 Model Validation and Analysis
I.6.8 Types of Simulation
I.6.8.h Parallel
Modeling
Object recognition
Parallel processing
ISSN:1521-9615
Online Access:Get full text
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Summary:To efficiently perform multiscale analysis of high-resolution, global, multiple-dimensional data sets, we have deployed the parallel ensemble empirical mode decomposition (PEEMD) package by implementing three-level parallelism into the ensemble Empirical Mode Decomposition (EMD), achieving a parallel speedup of 720x using 200 eight-core processors. In this study, we discuss the implementation of the PEEMD and its application for the analysis of Earth science data, including the solution of Lorenz model, an idealized terrain-induced flow and real case Hurricane Sandy (2012), the latter of which is the second costliest hurricane in the US history.
ISSN:1521-9615
DOI:10.1109/MCSE.2017.2581314