Cargando…
Parallel Computation of EM Backscattering from Large Three-Dimensional Sea Surface with CUDA
An efficient parallel computation using graphics processing units (GPUs) is developed for studying the electromagnetic (EM) backscattering characteristics from a large three-dimensional sea surface. A slope-deterministic composite scattering model (SDCSM), which combines the quasi-specular scatterin...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263511/ https://www.ncbi.nlm.nih.gov/pubmed/30373295 http://dx.doi.org/10.3390/s18113656 |
_version_ | 1783375310104821760 |
---|---|
author | Linghu, Longxiang Wu, Jiaji Wu, Zhensen Wang, Xiaobing |
author_facet | Linghu, Longxiang Wu, Jiaji Wu, Zhensen Wang, Xiaobing |
author_sort | Linghu, Longxiang |
collection | PubMed |
description | An efficient parallel computation using graphics processing units (GPUs) is developed for studying the electromagnetic (EM) backscattering characteristics from a large three-dimensional sea surface. A slope-deterministic composite scattering model (SDCSM), which combines the quasi-specular scattering of Kirchhoff Approximation (KA) and Bragg scattering of the two-scale model (TSM), is utilized to calculate the normalized radar cross section (NRCS in dB) of the sea surface. However, with the improvement of the radar resolution, there will be millions of triangular facets on the large sea surface which make the computation of NRCS time-consuming and inefficient. In this paper, the feasibility of using NVIDIA Tesla K80 GPU with four compute unified device architecture (CUDA) optimization strategies to improve the calculation efficiency of EM backscattering from a large sea surface is verified. The whole GPU-accelerated SDCSM calculation takes full advantage of coalesced memory access, constant memory, fast math compiler options, and asynchronous data transfer. The impact of block size and the number of registers per thread is analyzed to further improve the computation speed. A significant speedup of 748.26x can be obtained utilizing a single GPU for the GPU-based SDCSM implemented compared with the CPU-based counterpart performing on the Intel(R) Core(TM) i5-3450. |
format | Online Article Text |
id | pubmed-6263511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62635112018-12-12 Parallel Computation of EM Backscattering from Large Three-Dimensional Sea Surface with CUDA Linghu, Longxiang Wu, Jiaji Wu, Zhensen Wang, Xiaobing Sensors (Basel) Article An efficient parallel computation using graphics processing units (GPUs) is developed for studying the electromagnetic (EM) backscattering characteristics from a large three-dimensional sea surface. A slope-deterministic composite scattering model (SDCSM), which combines the quasi-specular scattering of Kirchhoff Approximation (KA) and Bragg scattering of the two-scale model (TSM), is utilized to calculate the normalized radar cross section (NRCS in dB) of the sea surface. However, with the improvement of the radar resolution, there will be millions of triangular facets on the large sea surface which make the computation of NRCS time-consuming and inefficient. In this paper, the feasibility of using NVIDIA Tesla K80 GPU with four compute unified device architecture (CUDA) optimization strategies to improve the calculation efficiency of EM backscattering from a large sea surface is verified. The whole GPU-accelerated SDCSM calculation takes full advantage of coalesced memory access, constant memory, fast math compiler options, and asynchronous data transfer. The impact of block size and the number of registers per thread is analyzed to further improve the computation speed. A significant speedup of 748.26x can be obtained utilizing a single GPU for the GPU-based SDCSM implemented compared with the CPU-based counterpart performing on the Intel(R) Core(TM) i5-3450. MDPI 2018-10-28 /pmc/articles/PMC6263511/ /pubmed/30373295 http://dx.doi.org/10.3390/s18113656 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Linghu, Longxiang Wu, Jiaji Wu, Zhensen Wang, Xiaobing Parallel Computation of EM Backscattering from Large Three-Dimensional Sea Surface with CUDA |
title | Parallel Computation of EM Backscattering from Large Three-Dimensional Sea Surface with CUDA |
title_full | Parallel Computation of EM Backscattering from Large Three-Dimensional Sea Surface with CUDA |
title_fullStr | Parallel Computation of EM Backscattering from Large Three-Dimensional Sea Surface with CUDA |
title_full_unstemmed | Parallel Computation of EM Backscattering from Large Three-Dimensional Sea Surface with CUDA |
title_short | Parallel Computation of EM Backscattering from Large Three-Dimensional Sea Surface with CUDA |
title_sort | parallel computation of em backscattering from large three-dimensional sea surface with cuda |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263511/ https://www.ncbi.nlm.nih.gov/pubmed/30373295 http://dx.doi.org/10.3390/s18113656 |
work_keys_str_mv | AT linghulongxiang parallelcomputationofembackscatteringfromlargethreedimensionalseasurfacewithcuda AT wujiaji parallelcomputationofembackscatteringfromlargethreedimensionalseasurfacewithcuda AT wuzhensen parallelcomputationofembackscatteringfromlargethreedimensionalseasurfacewithcuda AT wangxiaobing parallelcomputationofembackscatteringfromlargethreedimensionalseasurfacewithcuda |