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基于CPU/GPU异构模式的高光谱遥感影像数据处理研究与实现
引用本文:汤媛媛,周海芳,方民权,申小龙. 基于CPU/GPU异构模式的高光谱遥感影像数据处理研究与实现[J]. 计算机科学, 2016, 43(2): 47-50, 77
作者姓名:汤媛媛  周海芳  方民权  申小龙
作者单位:国防科学技术大学计算机学院 长沙410073;中国人民武装警察部队黄金地质研究所 廊坊065000,国防科学技术大学计算机学院 长沙410073,国防科学技术大学计算机学院 长沙410073,国防科学技术大学计算机学院 长沙410073
基金项目:本文受国家自然科学基金项目(61272146)资助
摘    要:近年来,基于GPU的新型异构高性能计算模式的蓬勃发展为众多领域应用提供了良好的发展机遇,国内外遥感专家开始引入高性能异构计算来解决高光谱遥感影像高维空间特点所带来的数据计算量大、实时处理难等问题。在此简要介绍了高光谱遥感和CPU/GPU异构计算模式,总结了近几年国内外基于CPU/GPU异构模式的高光谱遥感数据处理研究现状和问题;并面向共享存储型小型桌面超级计算机,基于CPU/GPU异构模式实现了高光谱遥感影像MNF降维的并行化,通过与串行程序和共享存储的OpenMP同构模式对比,验证了异构模式在高光谱遥感处理领域的发展潜力。

关 键 词:高光谱遥感  CPU/GPU  OpenMP  MNF
收稿时间:2015-03-01
修稿时间:2015-05-08

Hyperspectral Remote Sensing Image Data Processing Research and Realization Based on CPU/GPU Heterogeneous Model
TANG Yuan-yuan,ZHOU Hai-fang,FANG Min-quan and SHEN Xiao-long. Hyperspectral Remote Sensing Image Data Processing Research and Realization Based on CPU/GPU Heterogeneous Model[J]. Computer Science, 2016, 43(2): 47-50, 77
Authors:TANG Yuan-yuan  ZHOU Hai-fang  FANG Min-quan  SHEN Xiao-long
Affiliation:School of Computer Science,National University of Defense Technology,Changsha 410073,China;Gold Geological Institute of CAPF,Langfang 065000,China,School of Computer Science,National University of Defense Technology,Changsha 410073,China,School of Computer Science,National University of Defense Technology,Changsha 410073,China and School of Computer Science,National University of Defense Technology,Changsha 410073,China
Abstract:In recent years,the development of new high-performance heterogeneous computing based on GPU provides good oppotunities in many application areas.Domestic and foreign remote sensing experts have started to introduce it to solve the issues like computation intensive and difficult real-time processing caused by high-dimensional space features of hyperspectral image.In this brief introduction to hyperspectral remote sensing and CPU/GPU heterogeneous computing model,we summarized hyperspectral data processing status and problems based on CPU/GPU heterogeneous pattern in recent years,and for small desktop supercomputer with shared storage,realized parallelization of hyperspectral imaging MNF dimensionality reduction on CPU/GPU heterogeneous model,and verified the development potential of heterogeneous patterns in the field of hyperspectral remote sensing processing by contrasting with the sequential program and OpenMP.
Keywords:Hyperspectral remote sensing  CPU/GPU  OpenMP  MNF
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