@inproceedings{2453169c5b2d4f03a5985eac51813f66,
title = "Parallel implementation of multi-dimensional ensemble empirical mode decomposition",
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.",
keywords = "CUDA, GPGPU, Multi-dimensional Ensemble Empirical Mode Decomposition, OpenMP",
author = "Chang, {Li Wen} and Lo, {Men Tzung} and Nasser Anssari and Hsu, {Ke Hsin} and Huang, {Norden E.} and Hwu, {Wen Mei W.}",
year = "2011",
doi = "10.1109/ICASSP.2011.5946808",
language = "English (US)",
isbn = "9781457705397",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "1621--1624",
booktitle = "2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings",
note = "36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 ; Conference date: 22-05-2011 Through 27-05-2011",
}