TY - GEN
T1 - Implementation of bitsliced AES encryption on CUDA-Enabled GPU
AU - Nishikawa, Naoki
AU - Amano, Hideharu
AU - Iwai, Keisuke
PY - 2017
Y1 - 2017
N2 - Table-based implementations have been mainly reported in research related to high-performance AES on GPUs, in which tables are stored in the shared memory. On the other hand, this kind of implementations is subject to timing attacks, due to the latency required to access tables in the shared memory. Thanks to the increasing number of registers every year, GPU programming has enabled memory intensive applications such as bitsliced AES algorithm to be easily implemented. However, researches of implementation of bitsliced AES algorithm on GPU have not so far been conducted sufficiently in terms of several parameters. For this reason, in this paper, we present an implementation of bitsliced AES encryption on CUDA-enabled GPU with several parameters, especially focusing on three kinds of parallel processing granularities. According to the conducted experiments, the throughput of bitsliced AES-ECB encryption with Bs64 granularity achieves 605.9 Gbps on Nvidia Tesla P100-PCIe resulting in an enhancement of 8.0% when compared to the table-based implementation.
AB - Table-based implementations have been mainly reported in research related to high-performance AES on GPUs, in which tables are stored in the shared memory. On the other hand, this kind of implementations is subject to timing attacks, due to the latency required to access tables in the shared memory. Thanks to the increasing number of registers every year, GPU programming has enabled memory intensive applications such as bitsliced AES algorithm to be easily implemented. However, researches of implementation of bitsliced AES algorithm on GPU have not so far been conducted sufficiently in terms of several parameters. For this reason, in this paper, we present an implementation of bitsliced AES encryption on CUDA-enabled GPU with several parameters, especially focusing on three kinds of parallel processing granularities. According to the conducted experiments, the throughput of bitsliced AES-ECB encryption with Bs64 granularity achieves 605.9 Gbps on Nvidia Tesla P100-PCIe resulting in an enhancement of 8.0% when compared to the table-based implementation.
KW - AES
KW - Bitslice
KW - Cryptography
KW - CUDA
KW - GPU
UR - http://www.scopus.com/inward/record.url?scp=85028448525&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028448525&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-64701-2_20
DO - 10.1007/978-3-319-64701-2_20
M3 - Conference contribution
AN - SCOPUS:85028448525
SN - 9783319647005
VL - 10394 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 273
EP - 287
BT - Network and System Security - 11th International Conference, NSS 2017, Proceedings
PB - Springer Verlag
T2 - 11th International Conference on Network and System Security, NSS 2017
Y2 - 21 August 2017 through 23 August 2017
ER -