IMPLEMENTATION OF THE LORENTZ–DRUDE MODEL INCORPORATED FDTD METHOD ON MULTIPLE GPUs FOR PLASMONICS APPLICATIONS

Abstract

The Lorentz–Drude model incorporated Maxwell equations are simulated by using the three-dimensional finite difference time domain (FDTD) method and the method is parallelized on multiple graphics processing units (GPUs) for plasmonics applications. The compute unified device architecture (CUDA) is used for GPU parallelization. The Lorentz–Drude (LD) model is used to simulate the dispersive nature of materials in plasmonics domain and the auxiliary differential equation (ADE) approach is used to make it consistent with time domain Maxwell equations. Different aspects of multiple GPUs for the FDTD method are presented such as comparison of different numbers of GPUs, transfer time in between them, synchronous, and asynchronous passing. It is shown that by using multiple GPUs in parallel fashion, significant reduction in the simulation time can be achieved as compared to the single GPU.

A multiple-data-based efficient global optimization algorithm and its parallel implementation for automotive body design

“…In recent years, graphics processing units (GPUs) have become a novel parallel tool since they offer a tremendous amount of computing resources not only for graphics processes but also for general-purpose parallel computations. 16,17 Actually, one of the promising trends in the field of parallel global optimization is the use of GPUs to enhance the speed of computation. 18 For example, Barkalov et al 19 presented a parallel global optimization algorithm combined with a dimension reduction scheme.…”