1462

Fast GPU Implementation of Sparse Signal Recovery from Random Projections

M. Andrecut
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}

}

Download Download (PDF)   View View   Source Source   

572

views

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).
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: