Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons
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DOI：
10.3390/rs10060864
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发表时间：
2018-06-01
期刊：
影响因子：
5
通讯作者：
Sarmiento, Roberto
中科院分区：
文献类型：
--
作者：
Martel, Ernestina;Lazcano, Raquel;Sarmiento, Roberto
降维是提高许多高光谱成像算法的效率和性能的关键预处理步骤。然而，降维算法（例如主成分分析 (PCA)）因其计算要求高而受到影响，因此建议在严格延迟限制下的应用程序的高性能计算机架构上实现它们。这项工作展示了 PCA 算法在两种不同的高性能设备上的实现，即 NVIDIA 图形处理单元 (GPU) 和 Kalray 众核，揭示了一组非常有价值的提示和技巧，以便充分利用这些高性能计算平台固有的并行性，从而减少处理给定高光谱图像所需的时间。此外，将不同高光谱图像获得的结果与最近发布的基于现场可编程门阵列（FPGA）的PCA算法实现获得的结果进行了比较，在文献中首次提供了全面的分析，以突出每个选项的优缺点。
Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA), suffer from their computationally demanding nature, becoming advisable for their implementation onto high-performance computer architectures for applications under strict latency constraints. This work presents the implementation of the PCA algorithm onto two different high-performance devices, namely, an NVIDIA Graphics Processing Unit (GPU) and a Kalray manycore, uncovering a highly valuable set of tips and tricks in order to take full advantage of the inherent parallelism of these high-performance computing platforms, and hence, reducing the time that is required to process a given hyperspectral image. Moreover, the achieved results obtained with different hyperspectral images have been compared with the ones that were obtained with a field programmable gate array (FPGA)-based implementation of the PCA algorithm that has been recently published, providing, for the first time in the literature, a comprehensive analysis in order to highlight the pros and cons of each option.