The use of general-purpose computing on a GPU is an effective way to accelerate the FDTD method. This paper introduces flexibility to the theoretically best available approach. It examines the performance on both Tesla-and Fermi-architecture GPUs, and identifies the best way to determine the GPU parameters for the proposed method.
All Science Journal Classification (ASJC) codes
- Condensed Matter Physics
- Electrical and Electronic Engineering