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Synchronous, asynchronous and grouping asynchronous parallel implementation for N-FINDR algorithms in hyperspectral remote sensing image

机译:高光谱遥感图像中N-FindR算法的同步,异步和分组异步并行实现

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N-FINDR is a widely used endmember extraction algorithm in hyperspectral imagery. Nevertheless, its computational complexity is high. Plaza's parallel implementation of NFINDR, namely, P-FINDR, demonstrates an excellent way to improve the computing performance of N-FINDR by incorporating with parallel computing technique. In this paper, three parallel implementation patterns, i.e., synchronous, asynchronous and grouping asynchronous pattern, are presented. Two versions of N-FINDR, i.e. iterative N-FINDR and successive N-FINDR, are considered to be implemented in parallel by these patterns respectively. Thus obtains six parallel N-FINDR algorithms. In experiment, both solution quality and parallel performance of these algorithms are compared and suitable patterns for parallel implementation of N-FINDR are obtained.
机译:N-FindR是高光谱图像中广泛使用的EndMember提取算法。然而,其计算复杂性很高。 Plaza并行实现NFINDR,即P-FindR,通过结合并行计算技术来提高N-FindR的计算性能的优异方法。在本文中,提出了三种并行实现模式,即同步,异步和分组异步模式。两个版本的N-FindR,即迭代N-FindR和连续的N-FindR,被认为分别由这些模式并行地实现。因此获得六个并行N-FindR算法。在实验中,比较了这些算法的溶液质量和并行性能,并获得了用于平行实施的N-FindR的合适模式。

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