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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
University of Illinois at Urbana-Champaign, USA 61801
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

@inproceedings{chang2011parallel,

   title={Parallel Implementation of Multi-dimensional Ensemble Empirical Mode Decomposition},

   author={Chang, L.W. and Lo, M.T. and Anssari, N. and Hsu, K.H. and Huang, N.E. and Wen-mei, W.H.},

   booktitle={International Conference on Acoustics, Speech, and Signal Processing},

   year={2011}

}

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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.
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