SAR image simulations using the LBM Algorithm on MPI-GPU

NCUIR > Research Center > Center for Space and Remote Sensing Research > journal & Dissertation > Item 987654321/101693

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/101693

Title:	SAR image simulations using the LBM Algorithm on MPI-GPU
Authors:	蔡龍治;Sun, Chuan-Li;Tsai, Lung-Chih;Chiang, Cheng-Yen
Contributors:	太空及遙測研究中心
Keywords:	Algorithms;Central processing units;Computation;Computer applications;Computer simulation;CPUs;Data analysis;Data processing;Echoes;Graphics;Graphics processing units;Message passing;Natural environment;Object recognition;Radar;Radar cross sections;Radar imaging;Radarsat;SAR (radar);Simulation;Stability;Synthetic aperture radar;Systems design;Target recognition
Date:	2016-08-01
Issue Date:	2026-04-21 14:40:57 (UTC+8)
Publisher:	Chinese Geoscience Union;Taiwan: Springer Nature B.V
Abstract:	摘要： Synthetic Aperture Radar (SAR) is a powerful tool for studying natural environments under all-weather and day-and-night conditions. SAR system design and data-processing algorithm simulation is noted for its controllable parameters. The satellite SAR echo signal simulation framework has been successfully applied to target recognition based on Radarsat-2 and TerraSAR-X images and in strip map mode. However, such SAR image simulation works only on CPU or GPU (graphics processing units) and requires huge calculations. We developed a “Load-Balancing Model (LBM)” algorithm that uses Message Passing Interface GPU (MPI-GPU) to reduce the inner loop load and improve the computational performance. The LBM algorithm uses MPI-GPU technology to build the simple GPU cluster system. The LBM algorithm is used to separate the intensive computing and controlling tasks for each node, and exploit the contemporary GPU computation capability to accelerate the computing tasks. We conducted a relevant experiment on a target radar cross section (RCS) and improved the performance by a factor of > 40 compared to a 4-core CPU accelerated program. Key points • The LBM algorithm with MPI and CUDA to build a parallel computing environment • The overall computation will complete in the shortest amount of time • The time complexity contributed from the calculation kernel is O(n3) reduced to O(n2) 出版者： Taiwan: Springer Nature B.V 出版日期： 2016-08-01 出處： TAO : Terrestrial, atmospheric, and oceanic sciences, 2016-08, Vol.27 (4), p.577 資源來源： Agricultural & Environmental Science Collection 版權： 2016. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 識別號： ISSN: 1017-0839 識別號： ISSN: 2223-8964 識別號： ISSN: 2311-7680 識別號： EISSN: 2311-7680 識別號： DOI: 10.3319/TAO.2016.03.10.01(ISRS)
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	47	View/Open

社群 sharing

Loading...