Accelerated 2D Image Processing on GPUs

  • Bryson R. Payne
  • Saeid O. Belkasim
  • G. Scott Owen
  • Michael C. Weeks
  • Ying Zhu
Conference paper

DOI: 10.1007/11428848_32

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3515)
Cite this paper as:
Payne B.R., Belkasim S.O., Owen G.S., Weeks M.C., Zhu Y. (2005) Accelerated 2D Image Processing on GPUs. In: Sunderam V.S., van Albada G.D., Sloot P.M.A., Dongarra J.J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3515. Springer, Berlin, Heidelberg

Abstract

Graphics processing units (GPUs) in recent years have evolved to become powerful, programmable vector processing units. Furthermore, the maximum processing power of current generation GPUs is roughly four times that of current generation CPUs (central processing units), and that power is doubling approximately every nine months, about twice the rate of Moore’s law. This research examines the GPU’s advantage at performing convolutionbased image processing tasks compared to the CPU. Straight-forward 2D convolutions show up to a 130:1 speedup on the GPU over the CPU, with an average speedup in our tests of 59:1. Over convolutions performed with the highly optimized FFTW routines on the CPU, the GPU showed an average speedup of 18:1 for filter kernel sizes from 3x3 to 29x29.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bryson R. Payne
    • 1
  • Saeid O. Belkasim
    • 2
  • G. Scott Owen
    • 2
  • Michael C. Weeks
    • 2
  • Ying Zhu
    • 2
  1. 1.Department of ISCMGeorgia College & State UniversityMilledgeville
  2. 2.Department of Computer ScienceGeorgia State UniversityAtlanta

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