Improving Cryptanalytic Applications with Stochastic Runtimes on GPUs

Abstract

We investigate cryptanalytic applications comprised of many independent tasks that exhibit a stochastic runtime distribution. We compare four algorithms for executing such applications on GPUs. We demonstrate that for different distributions, problem sizes, and platforms the best strategy varies. We support our analytic results by extensive experiments on two different GPUs, from different sides of the performance spectrum: A high performance GPU (Nvidia Volta) and an energy saving system on chip (Jetson Nano).