Year: 2024
Type: conference-paper
Abstract: As wireless networks continue to expand, efficiently solving routing optimization problems has become a significant challenge. The sparse matrix equations generated by large-scale network topologies require efficient algorithms during the solution process. Although traditional Krylov subspace methods, such as the Conjugate Gradient method (CG), Generalized Minimal Residual method (GMRES), and Bi-Conjugate Gradient Stabilized method (BiCGSTAB), are capable of handling sparse matrices, they often suffer from slow convergence rates and high computational costs when faced with large-scale networks and complex matrix conditions. This paper proposes a GPU-accelerated IDR(s) algorithm to solve sparse matrix equations in wireless network routing optimization. By introducing multidimensional residuals and a flexible residual management mechanism, IDR(s) effectively reduces the number of iterations. Additionally, the algorithm leverages the parallel computing capabilities of GPUs to enhance computational efficiency. The GPU-based IDR(s) algorithm is applied to routing optimization problems in modern wireless networks.
Author: xia Wang
Institution: Beijing University of Posts and Telecommunications
Language: English
Topic: Matrix Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410858512