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Accelerating MATLAB Image Processing Toolbox functions on GPUs

Jingfei Kong, Martin Dimitrov, Yi Yang, Janaka Liyanage, Lin Cao, Jacob Staples, Mike Mantor, Huiyang Zhou
School of Computer Science,University of Central Florida
ACM International Conference Proceeding Series Vol 425 (2010)

@conference{kong2010accelerating,

   title={Accelerating MATLAB Image Processing Toolbox functions on GPUs},

   author={Kong, J. and Dimitrov, M. and Yang, Y. and Liyanage, J. and Cao, L. and Staples, J. and Mantor, M. and Zhou, H.},

   booktitle={Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units},

   pages={75–85},

   year={2010},

   organization={ACM}

}

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In this paper, we present our effort in developing an open-source GPU (graphics processing units) code library for the MATLAB Image Processing Toolbox (IPT). We ported a dozen of representative functions from IPT and based on their inherent characteristics, we grouped these functions into four categories: data independent, data sharing, algorithm dependent and data dependent. For each category, we present a detailed case study, which reveals interesting insights on how to efficiently optimize the code for GPUs and highlight performance-critical hardware features, some of which have not been well explored in existing literature. Our results show drastic speedups for the functions in the data-independent or data-sharing category by leveraging hardware support judiciously; and moderate speedups for those in the algorithm-dependent category by careful algorithm selection and parallelization. For the functions in the last category, fine-grain synchronization and data-dependency requirements are the main obstacles to an efficient implementation on GPUs.
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