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Proceedings Paper

Implementing two compressed sensing algorithms on GPU
Author(s): Sui Dong; Jun Ke; Ping Wei
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Paper Abstract

Compressed sensing (CS) is a new branch for information theory from the development of mathematical in 21st. CS provides a state-of-art technique that we can reconstruct sparse signal from a very limited number of measurements. In CS, reconstruct algorithm often need dense computation. The well-know algorithms like Basis Pursuit (BP) or Matching Pursuit (MP) is not likely to implement in PCs in practice. In this paper, we consider to use GPU (Graphic Processing Unit) and its large-scale computation ability to solve this problem. Based on the recently released NVIDIA CUDA 6.0 Tool Kit and CUBLAS library we study the GPU implementation of Orthogonal Matching Pursuit (OMP), and Two-Step Iterative Shrinkage algorithm (TwIST) implementing on GPU. The result shows that compared with CPU, implementing those algorithms on GPU can get an obvious speed up without losing any accuracy.

Paper Details

Date Published: 31 October 2014
PDF: 6 pages
Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92730J (31 October 2014); doi: 10.1117/12.2071432
Show Author Affiliations
Sui Dong, Beijing Institute of Technology (China)
Jun Ke, Beijing Institute of Technology (China)
Ping Wei, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 9273:
Optoelectronic Imaging and Multimedia Technology III
Qionghai Dai; Tsutomu Shimura, Editor(s)

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