GPU accelerated greedy algorithms for compressed sensing

  • 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 rocessing unit implementations using automatic multi-threading.

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Metadaten
Author:Jeffrey D. Blanchard, Jared Tanner
DOI:https://doi.org/10.1007/s12532-013-0056-5
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2013
Tag:Software; Theoretical Computer Science
Volume:5
Issue:3
Page Number:38
First Page:267
Last Page:304
Mathematical Programming Computation :MPC 2013 - Issue 3
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