Abstract Spectroscopies are of fundamental importance but can suffer from low sensitivity. Singular value decomposition (SVD) is a highly interesting mathematical tool, which can be conjugated with low-rank approximation to denoise spectra and increase sensitivity. SVD is also involved in data mining with principal component analysis (PCA). In this paper, we focused on the optimization of SVD duration, which is a time-consuming computation. Both Intel processors (CPU) and Nvidia graphic cards (GPU) were benchmarked. A 100 times gain was achieved when combining divide and conquer algorithm, Intel Math Kernel Library (MKL), SSE3 (Streaming SIMD Extensions) hardware instructions and single precision. In such a case, the CPU can outperform the GPU driven by CUDA technology. These results give a strong background to optimize SVD computation at the user scale. Graphical Abstract

中文翻译：

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应用于光谱学的降噪 - 第二部分：减少计算时间

摘要 光谱学具有重要意义，但灵敏度低。奇异值分解 (SVD) 是一种非常有趣的数学工具，它可以与低秩近似共轭，以对频谱进行降噪并提高灵敏度。SVD 还涉及使用主成分分析 (PCA) 进行数据挖掘。在本文中，我们专注于 SVD 持续时间的优化，这是一项耗时的计算。英特尔处理器 (CPU) 和 Nvidia 显卡 (GPU) 都进行了基准测试。将分而治之算法、英特尔数学内核库 (MKL)、SSE3（流式 SIMD 扩展）硬件指令和单精度结合使用时，实现了 100 倍的增益。在这种情况下，CPU 可以胜过由 CUDA 技术驱动的 GPU。这些结果为在用户规模上优化 SVD 计算提供了强大的背景。图形概要