Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons
<|reference_start|>Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons: 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.<|reference_end|>
@article{martel2024implementation,
    title={Implementation of the Principal Component Analysis onto High-Performance
  Computer Facilities for Hyperspectral Dimensionality Reduction: Results and
  Comparisons},
    author={E. Martel, R. Lazcano, J. Lopez, D. Madro~nal, R. Salvador, S. Lopez,
  E. Juarez, R. Guerra, C. Sanz, R. Sarmiento},
    journal={arXiv preprint arXiv:2403.18321},
    year={2024},
    doi={10.3390/rs10060864},
    archivePrefix={arXiv},
    eprint={2403.18321},
    primaryClass={cs.LG cs.CV}
}