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High-Performance Symmetric Block Ciphers on Multicore CPU and GPUs

Naoki Nishikawa, Keisuke Iwai, Takakazu Kurokawa
Dept. of Computer Science, National Defense Academy of Japan, 1-10-20 Hashirimizu Yokosuka Kanagawa 239-8686, Japan
International Journal of Networking and Computing, Vol. 2, No. 2, 2012

@article{nishikawa2012high,

   title={High-Performance Symmetric Block Ciphers on Multicore CPU and GPUs},

   author={Nishikawa, N. and Iwai, K. and Kurokawa, T.},

   journal={International Journal of Networking and Computing},

   volume={2},

   number={2},

   pages={pp–251},

   year={2012}

}

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As the data protection with encryption becomes important day by day, the encryption processing using General Purpose computation on a Graphic Processing Unit (GPGPU) has been noticed as one of the methods to realize high-speed data protection technology. GPUs have evolved in recent years into powerful parallel computing devices, with a high cost-performance ratio. However, many factors affect GPU performance. In earlier work to gain higher AES performance using GPGPU in various ways, we obtained the following two technical viewpoints: (1) 16 Bytes/Thread is the best granularity (2) Extended key and substitution table stored in shared memory and plaintext stored in register are the best memory allocation style. However, AES is not the only cipher algorithm widely used in the real world. For this reason, this study was undertaken to test the hypothesis that these two findings are applicable to implementation of other symmetric block ciphers on two generation of GPU. In this study, we targeted five 128-bit symmetric block ciphers, AES, Camellia, CIPHERUNICORN-A, Hierocrypt-3, and SC2000, from an e-government recommended ciphers list by the CRYPTography Research and Evaluation Committees (CRYPTREC) in Japan. We evaluated the performance of these five symmetric block ciphers on the machine including a 4-core CPU and each GPU using three method: (A) throughput without data transfer, (B) throughput with data transfer and overlapping encryption processing on GPU, (C) throughput with data transfer and non-overlapping encryption processing on GPU. Results demonstrate that the throughput of implementation of SC2000 in method (A) on Tesla C2050 achieved extremely high 73.4 Gbps. Additionally, the throughput obtained using methods (B) and (C) deteriorated to 33.4 Gbps and 18.3 Gbps, respectively. Method (B) showed effective throughput with an approximately 4.7 times higher speed compared to that obtained when using 8 threads on a 4-core CPU.
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