@inproceedings{088572077f434a7dbcfb8d3e4928f369,
title = "Implementing two compressed sensing algorithms on GPU",
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.",
keywords = "CUDA, Compressed Sensing, GPU, OMP, TwIST",
author = "Sui Dong and Jun Ke and Ping Wei",
note = "Publisher Copyright: {\textcopyright} 2014 SPIE.; Optoelectronic Imaging and Multimedia Technology III ; Conference date: 09-10-2014 Through 11-10-2014",
year = "2014",
doi = "10.1117/12.2071432",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Qionghai Dai and Tsutomu Shimura",
booktitle = "Optoelectronic Imaging and Multimedia Technology III",
address = "United States",
}