Evaluation of GPU-Based Empirical Mode Decomposition for Off-Line Analysis

Pulung WASKITO  Shinobu MIWA  Yasue MITSUKURA  Hironori NAKAJO  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.12   pp.2328-2337
Publication Date: 2011/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.2328
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)
Empirical Mode Decomposition (EMD),  Hilbert-Huang Transform (HHT),  GPU,  CUDA,  

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In off-line analysis, the demand for high precision signal processing has introduced a new method called Empirical Mode Decomposition (EMD), which is used for analyzing a complex set of data. Unfortunately, EMD is highly compute-intensive. In this paper, we show parallel implementation of Empirical Mode Decomposition on a GPU. We propose the use of “partial+total” switching method to increase performance while keeping the precision. We also focused on reducing the computation complexity in the above method from O(N) on a single CPU to O(N/P log (N)) on a GPU. Evaluation results show our single GPU implementation using Tesla C2050 (Fermi architecture) achieves a 29.9x speedup partially, and a 11.8x speedup totally when compared to a single Intel dual core CPU.