Parallelization of the finite-difference time-domain method for room acoustics modelling based on CUDA

The parallelization of the finite-difference time-domain (FDTD) method for room acoustic simulation using graphic processing units (GPUs) has been subject of study even prior to the introduction of GPGPU (general-purpose computing on GPUs) environments such as the compute unified device architecture (CUDA) from Nvidia. A mature architecture nowadays, CUDA offers enough flexibility and processing power to obtain important performance gains with naively ported serial CPU codes. However, careful implementation of the algorithm and appropriate usage of the different subsystems a GPU offers can lead to even further performance improvements. In this paper, we present a detailed study between different approaches to the parallelization of the FDTD method applied to room acoustics modelling, and we describe several optimization guidelines to improve the computation speed when using single precision and double precision floating point model data, nearly doubling the performance obtained by previously published implementations.