NTRU modular lattice signature scheme on CUDA GPUs

Wei Dai, Berk Sunar, John Schanck, William Whyte, Zhenfei Zhang
2016 2016 International Conference on High Performance Computing & Simulation (HPCS)  
In this work we show how to use Graphics Processing Units (GPUs) with Compute Unified Device Architecture (CUDA) to accelerate a lattice based signature scheme, namely, the NTRU modular lattice signature (NTRU-MLS) scheme. Lattice based schemes require operations on large vectors that are perfect candidates for GPU implementations. In addition, similar to most lattice based signature schemes, NTRU-MLS provides transcript security with a rejection sampling technique. With a GPU implementation,
more » ... are able to generate many candidates simultaneously, and hence mitigate the performance slowdown from rejection sampling. Our implementation results show that for the original NTRU-MLS parameter sets, we obtain a 2× improvement in the signing speed; for the revised parameter sets, where acceptance rate of rejection sampling is down to around 1%, our implementation can be as much as 47× faster than a CPU implementation.
doi:10.1109/hpcsim.2016.7568376 dblp:conf/ieeehpcs/DaiSSWZ16 fatcat:ai5h6nzzhvfphl33ddjsumbiwq