Paper Info
Title
Fast Gpu Implementation Of Sparse Signal Recovery From Random Projections
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).
Year
Venue
Keywords
2009
ENGINEERING LETTERS
GPU programming, Nvidia CUDA, sparse signal recovery, random projections, matching pursuit algorithm
Field
DocType
Volume
Small number,Matching pursuit,Combinatorial optimization problem,Computer science,CUDA,Parallel computing,Signal recovery
Journal
17
Issue
ISSN
Citations 
3
1816-093X
11
PageRank 
References 
Authors
1.02
2
1
Name
Order
Citations
PageRank
Mircea Andrecut1738.52