A Compressive Sensing Algorithm for Many-Core Architectures

  • A. Borghi
  • J. Darbon
  • S. Peyronnet
  • T. F. Chan
  • S. Osher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6454)


This paper describes a parallel algorithm for solving the l 1-compressive sensing problem. Its design takes advantage of shared memory, vectorized, parallel and many-core microprocessors such as Graphics Processing Units (GPUs) and standard vectorized multi-core processors (e.g. quad-core CPUs). Experiments are conducted on these architectures, showing evidence of the efficiency of our approach.


Graphic Processing Unit Discrete Cosine Transform Compressive Sensing Sparse Signal Cache Memory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Candès, E., Romberg, J., Tao, T.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. on Information Theory 52, 489–509 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Tropp, J.: Just relax: Convex programming methods for identifying sparse signals. IEEE Trans. on Information Theory 51, 1030–1051 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. on PAMI 31, 210–227 (2009)CrossRefGoogle Scholar
  4. 4.
    Cevher, V., Sankaranarayanan, A., Duarte, M.F., Reddy, D., Baraniuk, R.G., Chellappa, R.: Compressive sensing for background subtraction. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 155–168. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine 58, 1182–1195 (2007)CrossRefGoogle Scholar
  6. 6.
    Combettes, P., Pesquet, J.C.: Proximal thresholding algorithm for minimization over orthonormal bases. SIAM J. on Opt. 18, 1351–1376 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Figueiredo, M., Nowak, R., Wright, S.: Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Sig. Proc. 1 (2007)Google Scholar
  8. 8.
    Hiriart-Urruty, J.B., Lemaréchal, C.: Convex Analysis and Minimization Algorithms. Springer, Heidelberg (1996)zbMATHGoogle Scholar
  9. 9.
    Borghi, A., Darbon, J., Peyronnet, S., Chan, T., Osher, S.: A simple compressive sensing algorithm for parallel many-core architectures. Technical report, UCLA (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • A. Borghi
    • 1
  • J. Darbon
    • 2
  • S. Peyronnet
    • 1
  • T. F. Chan
    • 3
  • S. Osher
    • 4
  1. 1.LRI, INRIAUniversité Paris SudOrsayFrance
  2. 2.CMLA, ENS Cachan, CNRSPRES UniverSudFrance
  3. 3.Science and TechnologyHong Kong UniversityHong Kong
  4. 4.Mathematics DepartmentUCLAUSA

Personalised recommendations