Accelerated 2d image processing on GPUs

Authors:
Bryson R. Payne;Saeid O. Belkasim;G. Scott Owen;Michael C. Weeks;Ying Zhu
Affiliations:
Department of ISCM, Georgia College & State University, Milledgeville, GA;Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Year:
2005

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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.