On the performance of GPU public-key cryptography
Samuel Neves, Filipe Araújo · 2011
Graphics processing units (GPUs) have become increasingly popular over the last years as a cost-effective means of accelerating various computationally intensive tasks. We study the particular case of modular exponentiation, the crucial operation behind most modern public-key cryptography algorithms. We focus our attention on the NVIDIA GT200 architecture, currently one of the most popular for general purpose GPU computation. We report our efforts to run modular exponentiation faster than any other method we were aware of for GPUs. Part of our performance advantage results from a different interleaving of the Montgomery multiplication, which was neglected in previous literature. The other part comes from carefully exploring general techniques, like loop unrolling and inline PTX assembly. Our throughput results, at over 20000 RSA-1024 decryptions per second or 41426 512-bit modular exponentiations per second, present a significant speedup over previous GPU implementations, without any significant latency penalty. Lastly, we evaluate our results in light of several popular metrics, namely performance/price and performance/watt ratios. We find that, while current GPUs generally perform better than CPUs, they show worse performance/watt ratios.