Fast GPU Implementation of Sparse Signal Recovery from Random Projections
Quantitative Methods
2009-01-25 v2 Data Analysis, Statistics and Probability
Abstract
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).
Cite
@article{arxiv.0809.1833,
title = {Fast GPU Implementation of Sparse Signal Recovery from Random Projections},
author = {M. Andrecut},
journal= {arXiv preprint arXiv:0809.1833},
year = {2009}
}
Comments
accepted for publication in Engineering Letters, 8 pages, code included, references added