Impact of GPU memory access patterns on FDTD

The application of General Purpose computing on a GPU is an effective way to accelerate the FDTD method. This work explores the different domain decomposition techniques from the literature and extends the theoretically best approach with additional flexibility. We examine the performance on both Tesla and Fermi architecture GPUs and identify the best way to determine the GPU parameters for the proposed method.