<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Partial least squares are the most widely used identification algorithm, but the algorithm cannot achieve real-time performance for massive data. To solve this application contradiction, a parallel computing strategy based on NVIDIA CU-DA architecture is proposed to implement the partial least squares algorithm using a graphics processor (GPU) with massively parallel computing features as the computing device and combining the advantages of GPU memory. Research and analysis found that the partial least squares algorithm implemented using CUDA on GPU is 48 times faster than the implementation of the CPU. Therefore, the algorithm has good usability and higher application value, which makes it possible to apply the partial least squares algorithm to massive data statistics.</p>
</abstract>

Partial least squares are the most widely used identification algorithm, but the algorithm cannot achieve real-time performance for massive data. To solve this application contradiction, a parallel computing strategy based on NVIDIA CU-DA architecture is proposed to implement the partial least squares algorithm using a graphics processor (GPU) with massively parallel computing features as the computing device and combining the advantages of GPU memory. Research and analysis found that the partial least squares algorithm implemented using CUDA on GPU is 48 times faster than the implementation of the CPU. Therefore, the algorithm has good usability and higher application value, which makes it possible to apply the partial least squares algorithm to massive data statistics.

A statistical method for massive data based on partial least squares algorithm

Heilongjiang Bayi Agricultural University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A statistical method for massive data based on partial least squares algorithm"}

Partial least squares are the most widely used identification algorithm, but the algorithm cannot achieve real-time performance for massive data. To solve this...

A statistical method for massive data based on partial least squares algorithm

Pubblicato online: 29 lug 2023

Pagine: -

Ricevuto: 27 ago 2022

Accettato: 06 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00102

Parole chiave
Massive data statistics, Partial least squares algorithm, CUDA, Graphics processor

© 2023 Yan Xu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A statistical method for massive data based on partial least squares algorithm

Pubblicato online: 29 lug 2023

Pagine: -

Ricevuto: 27 ago 2022

Accettato: 06 feb 2023

DOI: https://doi.org/10.2478/amns.2023.2.00102

Parole chiaveMassive data statistics, Partial least squares algorithm, CUDA, Graphics processor

© 2023 Yan Xu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Massive data statistics, Partial least squares algorithm, CUDA, Graphics processor