31 October 2014 Implementing two compressed sensing algorithms on GPU
Author Affiliations +
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.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sui Dong, Sui Dong, Jun Ke, Jun Ke, Ping Wei, Ping Wei, } "Implementing two compressed sensing algorithms on GPU", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92730J (31 October 2014); doi: 10.1117/12.2071432; https://doi.org/10.1117/12.2071432
PROCEEDINGS
6 PAGES


SHARE
Back to Top