Efficient stochastic FDTD algorithm with optimised GPU acceleration

A robust implementation of a stochastic finite-difference time-domain (S-FDTD) technique on modern graphics processing units (GPU) is introduced in this paper for complex media with high levels of statistically-variable heterogeneities. The fea-tured method is compared to the multiple-realisation Monte-Carlo FDTD process, while both schemes are developed on an optimal CUDA (compute unified device architecture) pro-gramming platform. Therefore, via the GPU’s enhanced paral-lelised features, a significant speedup is attained with regard to serialised computations as verified by several numerical results.