Fast GPU Implementation of Sparse Signal Recovery from Random Projections
Institute for Biocomplexity and Informatics, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4, Canada
arXiv:0809.1833 [q-bio.QM] (10 Sep 2008)
@article{andrecut2008fast,
title={Fast GPU implementation of sparse signal recovery from random projections},
author={Andrecut, M.},
journal={Arxiv preprint arXiv:0809.1833},
year={2008},
publisher={Citeseer}
}
We consider the problem of sparse signal recovery from a small number of random projections (measurements). This is a well known NP-hard to solve combinatorial optimization problem. A frequently used approach is based on greedy iterative procedures, such as the Matching Pursuit (MP) algorithm. Here, we discuss a fast GPU implementation of the MP algorithm, based on the recently released NVIDIA CUDA API and CUBLAS library. The results show that the GPU version is substantially faster (up to 31 times) than the highly optimized CPU version based on CBLAS (GNU Scientific Library).
November 13, 2010 by hgpu
