This paper presents the graphics processing unit (GPU) accelerated fundamental alternating-direction-implicit finite-difference time-domain (FADI-FDTD) with complex frequency shifted convolutional perfectly matched layer (CFS-CPML). The compact matrix form of the conventional ADI-FDTD method with CFS-CPML is formulated into FADI-FDTD with its right-hand-sides free of matrix operators, resulting in simpler and more concise update equations. Using Compute Unified Device Architecture (CUDA), the FADI-FDTD with CFS-CPML is further incorporated into the GPU to exploit data parallelism. Numerical results show that a much higher efficiency gain of up to 15 times can be achieved.
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