GPU-Based Acceleration on ACEnet for FDTD Method of Electromagnetic Field Analysis

• Main Author: Sun, Dachuan
• Language: en
• Published: 2013
• Subjects: GPU; FDTD; CUDA; parallel computing
• Online Access: http://hdl.handle.net/10222/42708

Graphics Processing Unit (GPU) programming techniques have been applied to a range of scientific and engineering computations. In computational electromagnetics, uses of the GPU technique have dramatically increased since the release of NVIDIA’s Compute Unified Device Architecture (CUDA), a powerful and simple-to-use programmer environment that renders GPU computing easy accessibility to developers not specialized in computer graphics. The focus of recent research has been on problems concerning the Finite-Difference Time-Domain (FDTD) simulation of electromagnetic (EM) fields. Traditional FDTD methods sometimes run slowly due to large memory and CPU requirements for modeling electrically large structures. Acceleration methods such as parallel programming are then needed. FDTD algorithm is suitable for multi-thread parallel computation with GPU. For complex structures and procedures, high performance GPU calculation algorithms will be crucial. In this work, we present the implementation of GPU programming for acceleration of computations for EM engineering problems. The speed-up is demonstrated through a few simulations with inexpensive GPUs and ACEnet, and the attainable efficiency is illustrated with numerical results. Using C, CUDA C, Matlab GPU, and ACEnet, we make comparisons between serial and parallel algorithms and among computations with and without GPU and CUDA, different types of GPUs, and personal computers and ACEnet. A maximum of 26.77 times of speed-up is achieved, which could be further boosted with development of new hardware in the future. The acceleration in run time will make many investigations possible and will pave the way for studies of large-scale computational electromagnetic problems that were previously impractical. This is a field that definitely invites more in-depth studies. === This is the thesis of my Master of Applied Science work at Dalhousie University.