CAJ | 학술논문

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。
고광보영상강유쾌속독립성분분석과정포함대규모구진운산화대량질대계산。통과분석산법열점，설계협방차구진계산、백화처리、ICA질대화IC변환등관건열점적도상처리단원영사방안，제출병실현일충G-FastICA병행산법，병기우GPU가구연구산법우화책략。실험결과현시：재처리고광보영상강유시， CPU／GPU이구계통능획득비CPU 경고효적성능，G-FastICA산법비천행최고가획득72배가속비，비16핵CPU병행처리쾌4～6．5배。
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.