Parallel implementation of multi-dimensional ensemble empirical mode decomposition

Li Wen Chang, Men Tzung Lo, Nasser Anssari, Ke Hsin Hsu, Norden E. Huang, Wen Mei W. Hwu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose and evaluate two parallel implementations of Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) for multi-core (CPU) and many-core (GPU) architectures. Relative to a sequential C implementation, our double precision GPU implementation, using the CUDA programming model, achieves up to 48.6x speedup on NVIDIA Tesla C2050. Our multi-core CPU implementation, using the OpenMP programming model, achieves up to 11.3x speedup on two octal-core Intel Xeon x7550 CPUs.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages1621-1624
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • CUDA
  • GPGPU
  • Multi-dimensional Ensemble Empirical Mode Decomposition
  • OpenMP

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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