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arXiv · 2206.06797

qrpca: A Package for Fast Principal Component Analysis with GPU Acceleration

Rafael S. de Souza·Xu Quanfeng·Shiyin Shen·Chen Peng·Zihao Mu
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Abstract

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.

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Rafael S. de Souza, Xu Quanfeng, Shiyin Shen, Chen Peng, Zihao Mu. 2022-09-06. qrpca: A Package for Fast Principal Component Analysis with GPU Acceleration. https://doi.org/10.1016/j.ascom.2022.100633

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