GPU-Accelerated Efficient Implementation of FDTD Methods With Optimum Time-Step Selection

The consistent combination of uneven space-time orders in finite-difference time-domain (FDTD) algorithms is the subject of this paper. When low-order time integration is used in conjunction with high-order spatial expressions, the operation of the numerical scheme close to the stability limit causes degraded performance and slow convergence. By exploiting accuracy considerations, we derive an estimate of the optimum-much smaller-time-step size that ameliorates errors in a mean-value sense and leads to improved precision. To deal with the augmentation of the required iterations, the parallel implementation of the FDTD techniques on graphics processing units is pursued, ensuring faster code executions and more efficient models.