GPU accelerated greedy algorithms for compressed sensing

Journal article

Abstract:: For appropriate matrix ensembles, greedy algorithms have proven to be an efficient means of solving the combinatorial optimization problem associated with compressed sensing. This paper describes an implementation for graphics processing units (GPU) of hard thresholding, iterative hard thresholding, normalized iterative hard thresholding, hard thresholding pursuit, and a two-stage thresholding algorithm based on compressive sampling matching pursuit and subspace pursuit. The GPU acceleration of the former bottleneck, namely the matrix-vector multiplications, transfers a significant portion of the computational burden to the identification of the support set. The software solves high-dimensional problems in fractions of a second which permits large-scale testing at dimensions currently unavailable in the literature. The GPU implementations exhibit up to 70× acceleration over standard Matlab central processing unit implementations using automatic multi-threading. © Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2013.

Language:: English
Keywords:: CoSaMP

Combinatorial optimization

HTP

Sparse approximation

Subspace pursuit

Greedy algorithms

Compressed sensing

Graphics processing units

Parallel computing

NIHT

IHT
Pubs id:: pubs:357783
UUID:: uuid:4c41b305-0bae-4966-99d8-b87d0453d694
Local pid:: pubs:357783
Source identifiers:: 357783
Deposit date:: 2014-02-14
ARK identifier:: ark:/29072/ora_4c41b3050bae496699d8b87d0453d694

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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