DocumentCode :
3542260
Title :
PyGASP: Python-based GPU-accelerated signal processing
Author :
Bowman, Nathaniel ; Carrier, Erin ; Wolffe, Greg
Author_Institution :
Sch. of Comput., Grand Valley State Univ., Allendale, MI, USA
fYear :
2013
fDate :
9-11 May 2013
Firstpage :
1
Lastpage :
6
Abstract :
Computational science is the application of computing technology to evaluate mathematical models in order to solve problems in the scientific disciplines. Many scientific fields are experiencing an explosion of data, with signal processing being a crucial technique for aiding interpretation and for distinguishing meaningful information from noise. This process requires tools that can be easily used by researchers from all branches of science and which are fast enough to manage the enormous amount of data being generated. This paper presents such a toolkit: an intuitive, high-performance Python library for facilitating large-scale signal analysis. Of particular interest is a novel PyCUDA implementation of the Discrete Wavelet Transform (DWT), several applications of which are demonstrated in this paper.
Keywords :
discrete wavelet transforms; graphics processing units; parallel architectures; signal processing; DWT; PyCUDA implementation; PyGASP; Python-based GPU-accelerated signal processing; computational science; discrete wavelet transform; high-performance Python library; large-scale signal analysis; signal processing; Discrete cosine transforms; Discrete wavelet transforms; Graphics processing units; Libraries; Signal processing; Discrete Wavelet Transform; GPU; Signal Processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electro/Information Technology (EIT), 2013 IEEE International Conference on
Conference_Location :
Rapid City, SD
ISSN :
2154-0357
Print_ISBN :
978-1-4673-5207-9
Type :
conf
DOI :
10.1109/EIT.2013.6632683
Filename :
6632683
Link To Document :
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