GPU ACCELERATED UNCONDITIONALLY STABLE CRANK-NICOLSON FDTD METHOD FOR THE ANALYSIS OF THREE-DIMENSIONAL MICROWAVE CIRCUITS

Abstract

The programmable graphics processing unit (GPU) is employed to accelerate the unconditionally stable Crank-Nicolson finite-difference time-domain (CN-FDTD) method for the analysis of microwave circuits. In order to efficiently solve the linear system from the CN-FDTD method at each time step, both the sparse matrix vector product (SMVP) and the arithmetic operations on vectors in the bi-conjugate gradient stabilized (Bi-CGSTAB) algorithm are performed with multiple processors of the GPU. Therefore, the GPU based BI-CGSTAB algorithm can significantly speed up the CN-FDTD simulation due to parallel computing capability of modern GPUs. Numerical results demonstrate that this method is very effective and a speedup factor of 10 can be achieved.