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Performance Evaluation of Parallel AES Implementations over CUDA GPU Framework

Chakchai So-In, Sarayut Poolsanguan, Chartchai Poonriboon, Kanokmon Rujirakul, Comdet Phudphut
Department of Computer Science, Faculty of Science, Khon Kaen University, Maung, Khon Kaen, Thailand, 40002
JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 7, No. 5, pp. 501-511, 2013

@article{so2013performance,

   title={Performance Evaluation of Parallel AES Implementations over CUDA GPU Framework},

   author={So-In, Chakchai and Poolsanguan, Sarayut and Poonriboon, Chartchai and Rujirakul, Kanokmon and Phudphut, Comdet},

   journal={Int. J. of Digital Content Technology and its Applications},

   volume={7},

   number={5},

   pages={501–511},

   year={2013}

}

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With a high computational complexity of encryption algorithm, AES, especially for huge real-time data, GPU has recently offered an alternate computational system instead of a traditional CPU (thread), incurring a significant improvement in speeding up the computational intensive parallel data encryption in various aspects – tremendous number of processing cores and non-generic computational processing architecture design. NVIDIA has developed a generic GPU programming platform and programming model, CUDA; however, a traditional AES CUDA implementation does not specify efficient techniques to utilize the GPU parallelism. As a result, in this research, we evaluate the recent proposed parallel AES implementations over GPU. In addition, to avoid the frequent shared memory access, we offer the possibility to rearrange AES stages to utilize the AES parallelism resulting in a momentous improvement of encryption speed-up over traditional GPU, and especially CPU.
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