@conference {Venugopalan2015GPU-accele,
title = {{GPU Acceleration of Iterative Physical Optics-Based Electromagnetic Simulations}},
booktitle = {IEEE High Performance Extreme Computing Conference (HPEC)},
year = {2015},
month = {Sept},
pages = {1-6},
abstract = {High fidelity prediction of the link budget between a pair of transmitting and receiving antennas in dense and complex environments is computationally very intensive at high frequencies. Iterative physical optics (IPO) is a scalable solution for electromagnetic (EM) simulations with complex geometry. In this paper, an efficient and robust solution is presented to predict the link budget between antennas in a dense environment. Two Nvidia GPUs with different number of cores and device memory were targeted for benchmarking the performance of the IPO algorithm. The results indicate that the GPU implementation of the IPO algorithm is memory bound. Also, the K40c GPU only provides 2{\texttimes} speedup over the GTX650M for cases less than 25K triangles, although it has 7.5{\texttimes} more cores than the GTX650M. The Nvidia K40c GPU provides a best case speedup of 7366{\texttimes} for a model that consists of 25K triangles at f = 2.4GHz.},
keywords = {Acceleration, Antennas, complex geometry, computational electromagnetics, Computational modeling, electromagnetic simulations, EM simulations, Geometry, GPU acceleration, graphics processing units, high fidelity prediction, IPO, iterative methods, iterative physical optics, K40c GPU, link budget prediction, Nvidia GPUs, parallel processing, physical optics, receiving antenna, receiving antennas, Solid modeling, Surface impedance, transmitting antenna, transmitting antennas, waveguide propagation, Wi-Fi coverage},
author = {V. Venugopalan and C. Tokgoz}
}