论文标题
QRPCA：使用GPU加速的快速主体组件分析的软件包
qrpca: A Package for Fast Principal Component Analysis with GPU Acceleration
论文作者
论文摘要
我们提出QRPCA，这是一个快速可扩展的QR分解主组件分析软件包。该软件均用R和Python语言编写，它将火炬用于内部矩阵计算，并在可用时启用GPU加速度。 QRPCA分别提供与PRCOMP（R）和Sklearn（Python）软件包相似的功能。基准测试表明，QRPCA可以达到计算速度10-20 $ \ times $对于大维矩阵的速度比默认实现快，并且对于光谱数据立方体的标准分解而言，至少是两倍。 QRPCA源代码可免费提供给社区。
We present qrpca, a fast and scalable QR-decomposition principal component analysis package. The software, written in both R and python languages, makes use of torch for internal matrix computations, and enables GPU acceleration, when available. qrpca provides similar functionalities to prcomp (R) and sklearn (python) packages respectively. A benchmark test shows that qrpca can achieve computational speeds 10-20 $\times$ faster for large dimensional matrices than default implementations, and is at least twice as fast for a standard decomposition of spectral data cubes. The qrpca source code is made freely available to the community.
