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 × 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.
@article{arxiv.2206.06797,
title = {qrpca: A Package for Fast Principal Component Analysis with GPU Acceleration},
author = {Rafael S. de Souza and Xu Quanfeng and Shiyin Shen and Chen Peng and Zihao Mu},
journal= {arXiv preprint arXiv:2206.06797},
year = {2022}
}