Accelerated 2D Image Processing on GPUs
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.
- 2.Dalvi, A.: Intel targets 4 GHz barrier. Techtree, online news service (2003), Available online at http://www.techtree.com/techtree/jsp/showstory.jsp?storyid=3970
- 3.Frigo, M., Johnson, S.G.: FFTW: An Adaptive Software Architecture for the FFT. In: IEEE ICASSP Proceedings, vol. (3), pp. 1700–1703. IEEE Press, Los Alamitos (1998)Google Scholar
- 5.Mark, W.R., Glanville, R.S., Akeley, K., Kilgard, M.J.: Cg: a system for programming graphics hardware in a C-like language. ACM Transactions on Graphics, ACM Press, 896–907 (2003)Google Scholar
- 6.Moreland, K., Angel, E.: The FFT on a GPU. In: Proceedings of the ACM SIGGRAPH Conference on Graphics Hardware. Eurographics Association, pp. 112–119 (2003)Google Scholar
- 7.NVIDIA Corporation: GeForce 6800 Product Overview (2004), http://www.nvidia.com
- 8.Payne, B.R.: Accelerating Scientific Computation in Bioinformatics by Using Graphics Processing Units as Parallel Vector Processors Doctoral dissertation, Georgia State University, Dissertation Abstracts International (2004) (UMI. No. pending)Google Scholar