| 多核与众核上MNF并行算法与性能优化 |
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| 引用本文： | 方民权,张卫民,高畅,方建滨.多核与众核上MNF并行算法与性能优化[J].软件学报,2015,26(S2):247-256. |
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| 作者姓名： | 方民权 张卫民 高畅 方建滨 |
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| 作者单位： | 国防科学技术大学计算机学院, 湖南长沙 410073;国防科学技术大学海洋科学与工程研究院, 湖南长沙 410073,国防科学技术大学计算机学院, 湖南长沙 410073;国防科学技术大学海洋科学与工程研究院, 湖南长沙 410073,国防科学技术大学计算机学院, 湖南长沙 410073,国防科学技术大学计算机学院, 湖南长沙 410073 |
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| 基金项目： | 国家自然科学基金(61272146, 41375113) |
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| 摘 要： | 高光谱遥感影像降维最大噪声分数变换(maximum noise fraction rotation,简称MNF rotation)方法运算量大,耗时长.基于多核CPU与众核MIC(many integrated cores)平台,研究MNF算法的并行方案和性能优化.通过热点分析,针对滤波、协方差矩阵运算和MNF变换等热点,提出相应并行方案和多种优化策略,量化分析优化效果,设计MKL(math kernel library)库函数实现方案并测评其性能;设计并实现基于多核CPU的C-MNF和基于CPU/MIC的M-MNF并行算法.实验结果显示,C-MNF算法在多核CPU取得的加速比为58.9~106.4,而基于CPU/MIC异构系统的M-MNF算法性能最好,加速比最高可达137倍.
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| 关 键 词： | 集成众核 多核并行 高光谱影像降维 最大噪声分数变换 MKL性能分析 |
| 收稿时间： | 8/7/2015 12:00:00 AM |
| 修稿时间： | 2015/10/12 0:00:00 |
Parallelizing and Optimizing Maximum Noise Fraction Rotation on Multi-Cores and Many-Cores |
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| FANG Min-Quan,ZHANG Wei-Min,GAO Chang and FANG Jian-Bin.Parallelizing and Optimizing Maximum Noise Fraction Rotation on Multi-Cores and Many-Cores[J].Journal of Software,2015,26(S2):247-256. |
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| Authors: | FANG Min-Quan ZHANG Wei-Min GAO Chang and FANG Jian-Bin |
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| Affiliation: | School of Computer, National University of Defense Technology, Changsha 410073, China;Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha 410073, China,School of Computer, National University of Defense Technology, Changsha 410073, China;Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha 410073, China,School of Computer, National University of Defense Technology, Changsha 410073, China and School of Computer, National University of Defense Technology, Changsha 410073, China |
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| Abstract: | Maximum noise fraction (MNF) rotation is a classical method of hyperspectral image dimensionality reduction, and it needs a large amount of calculation and thus is time-consuming. This paper investigates the code transplantation and performance optimization for the maximum noise fraction algorithm on multi-core CPU and many integrated core (MIC) architecture. By analyzing hotspots of the MNF algorithm, parallel schemes are first designed for filtering, covariance matrix calculating and MNF transforming. Then, a series of optimization methods are presented and validated for various parallel schemes of different hotspots, including using math kernel library (MKL) functions. Finally, a C-MNF algorithm on multi-cores CPUs and an M-MNF algorithm on the CPU/MIC heterogeneous system are constructed. Experiments show that the C-MNF algorithm achieves impressive speedups (ranging from 58.9 to 106.4), and the M-MNF parallel algorithm runs the fastest, reaching a maximum speed-up of 137X. |
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| Keywords: | many integrated cores multi-cores parallel hyperspectral images dimensionality reduction maximum noise fraction rotation performance analysis for MKL |
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