摘要
高光谱影像降维快速独立成分分析过程包含大规模矩阵运算和大量迭代计算。通过分析算法热点，设计协方差矩阵计算、白化处理、ICA迭代和Ic变换等关键热点的图像处理单元映射方案，提出并实现一种G-FastICA并行算法，并基于GPU架构研究算法优化策略。实验结果显示：在处理高光谱影像降维时，CPU／GPU异构系统能获得比CPU更高效的性能，G—FastICA算法比串行最高可获得72倍加速比，比16核CPU并行处理快4～6．5倍。
Fast independent component analysis dimensionality reduction for hyperspectral image needs a large amount of matrix and iterative computation. By analyzing hotspots of the fast independent component analysis algorithm, such as covariance matrix calculation, white processing, ICA iteration and IC transformation, a GPU-oriented mapping scheme and the optimization strategy based on GPU-oriented algorithm on memory accessing and computation-communication overlapping were proposed. The performance impact of thread-block size was also investigated. Experimental results show that better performance was obtained when dealing with the hyperspectral image dimensionality reduction problem： the GPU-oriented fast independent component analysis algorithm can reach a speedup of 72 times than the sequential code on CPU, and it runs 4 -6. 5 times faster than the case when using a 16-core CPU.
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2015年第4期65-70,共6页
Journal of National University of Defense Technology
基金
国家自然科学基金资助项目(61272146
41375113
41305101)
关键词
图像处理单元
高光谱影像降维
快速独立成分分析
并行算法
性能优化
graphic processing unit
hyperspectral image dimensionality reduction
fast independent component analysis
parallel algorithm
performance optimization
