IEICE TRANSACTIONS on Information
Evaluation of GPU-Based Empirical Mode Decomposition for Off-Line Analysis
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.12 pp.2328-2337
- Publication Date
- 2011/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.2328
- Type of Manuscript
- Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)
- Category
Authors
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Pulung WASKITO, Shinobu MIWA, Yasue MITSUKURA, Hironori NAKAJO, "Evaluation of GPU-Based Empirical Mode Decomposition for Off-Line Analysis" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 12, pp. 2328-2337, December 2011, doi: 10.1587/transinf.E94.D.2328.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.2328/_p
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@ARTICLE{e94-d_12_2328,
author={Pulung WASKITO, Shinobu MIWA, Yasue MITSUKURA, Hironori NAKAJO, },
journal={IEICE TRANSACTIONS on Information},
title={Evaluation of GPU-Based Empirical Mode Decomposition for Off-Line Analysis},
year={2011},
volume={E94-D},
number={12},
pages={2328-2337},
abstract={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.},
keywords={},
doi={10.1587/transinf.E94.D.2328},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Evaluation of GPU-Based Empirical Mode Decomposition for Off-Line Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 2328
EP - 2337
AU - Pulung WASKITO
AU - Shinobu MIWA
AU - Yasue MITSUKURA
AU - Hironori NAKAJO
PY - 2011
DO - 10.1587/transinf.E94.D.2328
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2011
AB - 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.
ER -
English
