A CUDA-based GPU engine for gprMax: open source FDTD electromagnetic simulation software

Warren, Craig, Giannopoulos, Antonios, Gray, Alan, Giannakis, Iraklis, Patterson, Alan, Wetter, Laura and Hamrah, Andre (2018) A CUDA-based GPU engine for gprMax: open source FDTD electromagnetic simulation software. Computer Physics Communications Package, 237. pp. 208-218. ISSN 0010-4655

[img]
Preview
PDF
gprMax_GPU.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (743kB) | Preview

Abstract

The Finite-Difference Time-Domain (FDTD) method is a popular numerical modelling technique in computational electromagnetics. The volumetric nature of the FDTD technique means simulations often require extensive computational resources (both processing time and memory). The simulation of Ground Penetrating Radar (GPR) is one such challenge, where the GPR transducer, subsurface/structure, and targets must all be included in the model, and must all be adequately discretised. Additionally, forward simulations of GPR can necessitate hundreds of models with different geometries (A-scans) to be executed. This is exacerbated by an order of magnitude when solving the inverse GPR problem or when using forward models to train machine learning algorithms.

We have developed one of the first open source GPU-accelerated FDTD solvers specifically focused on modelling GPR. We designed optimal kernels for GPU execution using NVIDIA’s CUDA framework. Our GPU solver achieved performance throughputs of up to 1194 Mcells/s and 3405 Mcells/s on NVIDIA Kepler and Pascal architectures, respectively. This is up to 30 times faster than the parallelised (OpenMP) CPU solver can achieve on a commonly-used desktop CPU (Intel Core i7-4790K). We found the cost–performance benefit of the NVIDIA GeForce-series Pascal-based GPUs – targeted towards the gaming market – to be especially notable, potentially allowing many individuals to benefit from this work using commodity workstations. We also note that the equivalent Tesla-series P100 GPU – targeted towards data-centre usage – demonstrates significant overall performance advantages due to its use of high-bandwidth memory. The performance benefits of our GPU-accelerated solver were demonstrated in a GPR environment by running a large-scale, realistic (including dispersive media, rough surface topography, and detailed antenna model) simulation of a buried anti-personnel landmine scenario.

Item Type: Article
Additional Information: © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: CUDA, Finite-Difference, Time-Domain, GPR, GPGPU, GPU, NVIDIA
Subjects: Construction and engineering > Electrical and electronic engineering
Computing
Related URLs:
Depositing User: Iraklis Giannakis
Date Deposited: 19 Jan 2019 17:03
Last Modified: 15 Feb 2019 14:17
URI: http://repository.uwl.ac.uk/id/eprint/5755

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item

Menu