Parallel compressive sampling matching pursuit algorithm for compressed sensing signal reconstruction with OpenCL

Huang, Fang, Tao, Jian, Xiang, Yang, Liu, Peng, Dong, Lei and Wang, Lizhe 2017, Parallel compressive sampling matching pursuit algorithm for compressed sensing signal reconstruction with OpenCL, Journal of systems architecture, vol. 72, pp. 51-60, doi: 10.1016/j.sysarc.2016.07.002.

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Title Parallel compressive sampling matching pursuit algorithm for compressed sensing signal reconstruction with OpenCL
Author(s) Huang, Fang
Tao, Jian
Xiang, YangORCID iD for Xiang, Yang
Liu, Peng
Dong, Lei
Wang, Lizhe
Journal name Journal of systems architecture
Volume number 72
Start page 51
End page 60
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-01
ISSN 1383-7621
Keyword(s) compressive sensing
heterogeneous computing
many-core computing
Summary Compressive sensing (CS) is a new signal processing method, which was developed recent years. CS can sample signals with a frequency far below the Nyquist frequency. CS can also compress the signals while sampling, which can reduce the usage of resources for signal transmission and storage. However, the reconstruction algorithm used in the corresponding decoder is highly complex and computationally expensive. Thus, in some specific applications, e.g., remote sensing image processing for disaster monitoring, the CS algorithm usually cannot satisfy the time requirements on traditional computing platforms. Various studies have shown that many-core computing platforms such as OpenCL are among the most promising platforms that are available for real-time processing because of their powerful floating-point computing capabilities. In this study, we present the design and implementation of parallel compressive sampling matching pursuit (CoSaMP), which is an OpenCL-based parallel CS reconstruction algorithm, as well as some optimization strategies, such as access efficiency, numerical merge, and instruction optimization. Based on experiments using remote sensing images with different sizes, we demonstrated that the proposed parallel algorithm can achieve speedups of about 41 times and 58 times on AMD HD7350 and NVIDIA K20Xm platforms, respectively, without modifying the application code.
Language eng
DOI 10.1016/j.sysarc.2016.07.002
Field of Research 080399 Computer Software not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
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Document type: Journal Article
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