Implementing two compressed sensing reconstruct algorithms on GPU

Sui Dong; Jun Ke; Ping Wei

doi:10.1117/12.2071432

31 October 2014 Implementing two compressed sensing algorithms on GPU

Sui Dong, Jun Ke, Ping Wei

Author Affiliations +

Proceedings Volume 9273, Optoelectronic Imaging and Multimedia Technology III; 92730J (2014) https://doi.org/10.1117/12.2071432
Event: SPIE/COS Photonics Asia, 2014, Beijing, China

Abstract

Compressed sensing (CS) is a new branch for information theory from the development of mathematical in 21^st. 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.

Citation Download Citation

Sui Dong, Jun Ke, and Ping Wei "Implementing two compressed sensing algorithms on GPU", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92730J (31 October 2014); https://doi.org/10.1117/12.2071432

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