Stochastic first passage time accelerated with CUDA

TL;DR

This paper introduces a CUDA-accelerated algorithm for efficiently estimating stochastic first passage times, achieving significant speedups and enabling simulations of complex physical phenomena like Josephson junction switching.

Contribution

The paper presents a GPU-based method for stochastic first passage time estimation that scales well and significantly accelerates computations compared to traditional CPU approaches.

Findings

Achieves approximately 400x acceleration with a GTX980 GPU.

Enables simulation of switching current distributions in Josephson junctions.

Demonstrates scalability and efficiency of the CUDA implementation.

Abstract

The numerical integration of stochastic trajectories to estimate the time to pass a threshold is an interesting physical quantity, for instance in Josephson junctions and atomic force microscopy, where the full trajectory is not accessible. We propose an algorithm suitable for efficient implementation on graphical processing unit in CUDA environment. The proposed approach for well balanced loads achieves almost perfect scaling with the number of available threads and processors, and allows an acceleration of about 400x with a GPU GTX980 respect to standard multicore CPU. This method allows with off the shell GPU to challenge problems that are otherwise prohibitive, as thermal activation in slowly tilted potentials. In particular, we demonstrate that it is possible to simulate the switching currents distributions of Josephson junctions in the timescale of actual experiments.