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Performance evaluation of image processing algorithms on the GPU

Daniel Castano-Diez, Dominik Moser, Andreas Schoenegger, Sabine Pruggnaller, Achilleas S S. Frangakis
Computational and Structural Biology, European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany
Journal of structural biology, Volume 164, Issue 1, October 2008, Pages 153-160

@article{castano2008performance,

   title={Performance evaluation of image processing algorithms on the GPU},

   author={Casta{~n}o-D{‘i}ez, D. and Moser, D. and Schoenegger, A. and Pruggnaller, S. and Frangakis, A.S.},

   journal={Journal of structural biology},

   volume={164},

   number={1},

   pages={153–160},

   issn={1047-8477},

   year={2008},

   publisher={Elsevier}

}

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The graphics processing unit (GPU), which originally was used exclusively for visualization purposes, has evolved into an extremely powerful co-processor. In the meanwhile, through the development of elaborate interfaces, the GPU can be used to process data and deal with computationally intensive applications. The speed-up factors attained compared to the central processing unit (CPU) are dependent on the particular application, as the GPU architecture gives the best performance for algorithms that exhibit high data parallelism and high arithmetic intensity. Here, we evaluate the performance of the GPU on a number of common algorithms used for three-dimensional image processing. The algorithms were developed on a new software platform called “CUDA”, which allows a direct translation from C code to the GPU. The implemented algorithms include spatial transformations, real-space and Fourier operations, as well as pattern recognition procedures, reconstruction algorithms and classification procedures. In our implementation, the direct porting of C code in the GPU achieves typical acceleration values in the order of 10-20 times compared to a state-of-the-art conventional processor, but they vary depending on the type of the algorithm. The gained speed-up comes with no additional costs, since the software runs on the GPU of the graphics card of common workstations.
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