|標題:||Flexible Parallelized Empirical Mode Decomposition in CUDA for Hilbert Huang Transform|
|作者:||Huang, Kevin P. -Y.|
Wen, Charles H. -P.
College of Electrical and Computer Engineering
|摘要:||Hilbert-Huang Transform (HHT) is a process of adaptive analysis applicable to non-linear and non-stationary data such as voice and biomedical signals. Empirical Mode Decomposition (EMD) is a key in HHT and decomposes data into multiple Intrinsic Mode Functions (IMFs). Traditionally, EMD is computed on all data points in a serial manner, thus making its execution time grows at least linearly with the data size. In this work, a 3-stage parallelized EMD algorithm working on a CUDA architecture is proposed to improve performance over traditional EMD. Moreover, additional merging cubic spline interpolation (MCSI) and GPU acceleration techniques are also incorporated for achieving high parallelism and high accuracy. Experimental result shows that our parallelized EMD in CUDA achieves 37.9x and 33.7X speedups with 0.0051% and 0.002% errors on voice and EEG datasets of 1-million points, respectively.|
|期刊:||2014 IEEE INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS, 2014 IEEE 6TH INTL SYMP ON CYBERSPACE SAFETY AND SECURITY, 2014 IEEE 11TH INTL CONF ON EMBEDDED SOFTWARE AND SYST (HPCC,CSS,ICESS)|
|Appears in Collections:||Conferences Paper|