<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Multi-core processor is a new type of parallel nonlinear function. It has the characteristics of high efficiency, low power consumption and scalability. It has applications in several fields, such as industrial environments, military weapons systems, aerospace technology, and biomedicine. The core function of a multicore processor is an algorithm that can realize the expansion of the capacity of the isoparametric center and the lifting space, whose main idea is to optimize the spatial distribution by changing the input vector. Traditional solution methods generally use iterative or variational methods to solve nonlinear programming problems, but linear algebraic function models are widely used because of their simplicity and ease of numerical computation. The discrete and multi-attribute matrix combination algorithm can be used to realize the process of transforming complex problems into easier solutions. This paper introduces the basic concepts of linear programming and conducts simulation experiments with a multicore processor, and then makes improvements to a typical optimization problem. This paper analyzes the mathematical principles and steps of the nonlinear mapping algorithm; then MATLAB is used as a platform to study the design process of the multicore set-driven function. Finally it verifies that the method is feasible in solving complex models by arithmetic examples.</p>
</abstract>

Multi-core processor is a new type of parallel nonlinear function. It has the characteristics of high efficiency, low power consumption and scalability. It has applications in several fields, such as industrial environments, military weapons systems, aerospace technology, and biomedicine. The core function of a multicore processor is an algorithm that can realize the expansion of the capacity of the isoparametric center and the lifting space, whose main idea is to optimize the spatial distribution by changing the input vector. Traditional solution methods generally use iterative or variational methods to solve nonlinear programming problems, but linear algebraic function models are widely used because of their simplicity and ease of numerical computation. The discrete and multi-attribute matrix combination algorithm can be used to realize the process of transforming complex problems into easier solutions. This paper introduces the basic concepts of linear programming and conducts simulation experiments with a multicore processor, and then makes improvements to a typical optimization problem. This paper analyzes the mathematical principles and steps of the nonlinear mapping algorithm; then MATLAB is used as a platform to study the design process of the multicore set-driven function. Finally it verifies that the method is feasible in solving complex models by arithmetic examples.

Optimization of Linear Algebra Core Function Framework on Multicore Processors

Dongchang College of Liaocheng University

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Optimization of Linear Algebra Core Function Framework on Multicore Processors"}

Multi-core processor is a new type of parallel nonlinear function. It has the characteristics of high efficiency, low power consumption and scalability. It has...

Optimization of Linear Algebra Core Function Framework on Multicore Processors

Pubblicato online: 15 lug 2022

Pagine: 1585 - 1596

Ricevuto: 13 apr 2022

Accettato: 10 giu 2022

DOI: https://doi.org/10.2478/amns.2022.2.0148

Parole chiave
Multicore processors, Linear algebra, Core function framework

© 2023 Zhi Fang, published by Sciendo

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

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Optimization of Linear Algebra Core Function Framework on Multicore Processors

Pubblicato online: 15 lug 2022

Pagine: 1585 - 1596

Ricevuto: 13 apr 2022

Accettato: 10 giu 2022

DOI: https://doi.org/10.2478/amns.2022.2.0148

Parole chiaveMulticore processors, Linear algebra, Core function framework

© 2023 Zhi Fang, published by Sciendo

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

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Parole chiave
Multicore processors, Linear algebra, Core function framework