Data Parallel Three-Dimensional Cahn-Hilliard Field Equation Simulation on GPUs with CUDA

Abstract

Computational scientific simulations have long used parallel computers to increase their performance. Recently graphics cards have been utilised to provide this functionality. GPGPU APIs such as NVidia’s CUDA can be used to harness the power of GPUs for purposes other than computer graphics. GPUs are designed for processing twodimensional data. In previous work we have presented several two-dimensional Cahn-Hilliard simulations that each utilise different CUDA memory types and compared their results. In this paper we extend these ideas to three dimensions. As GPUs are not intended for processing threedimensional data arrays, the performance of the memory optimisations is expected to change. Here we present several three-dimensional Cahn-Hilliard simulations to explore the challenges and the performance of the different memory types in three-dimensions. The results show that the simulation design with the best performance in threedimensions uses a different memory type to the optimal two-dimensional simulation.