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 language | English (US) |
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Title of host publication | 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings |
Pages | 1621-1624 |
Number of pages | 4 |
DOIs | |
State | Published - 2011 |
Event | 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic Duration: May 22 2011 → May 27 2011 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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ISSN (Print) | 1520-6149 |
Other
Other | 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 |
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Country/Territory | Czech Republic |
City | Prague |
Period | 5/22/11 → 5/27/11 |
Keywords
- CUDA
- GPGPU
- Multi-dimensional Ensemble Empirical Mode Decomposition
- OpenMP
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering