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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Vydáno v:Computing in science & engineering s. 1
Hlavní autoři: Shen, Bo-Wen, Cheung, Samson, Wu, Yu-ling, Li, Jui-Lin, Kao, David
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 08.06.2017
Témata:
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
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Abstract 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.
AbstractList 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.
Author Kao, David
Cheung, Samson
Wu, Yu-ling
Shen, Bo-Wen
Li, Jui-Lin
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SubjectTerms 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
Title Parallel Implementation of the Ensemble Empirical Mode Decomposition (PEEMD) and Its Application for Earth Science Data Analysis
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