Open AccessA Parallel Strategy for a Genetic Algorithm in Routing Wavelength Assignment Problem Using GPU with CUDA
Author(s) -
Esdras La-Roque,
Cassio Batista,
Josivaldo de Souza Araújo
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2020.12175
Subject(s) - cuda , computer science , parallel computing , bottleneck , routing (electronic design automation) , heuristic , genetic algorithm , speedup , parallel algorithm , general purpose computing on graphics processing units , algorithm , computer network , embedded system , artificial intelligence , graphics , machine learning , computer graphics (images)
This paper presents a parallel strategy with a heuristic approach to reduce the execution time bottleneck of a routing and wavelength assignment problem in wavelength-division multiplexing networks of a previous work that uses a sequential genetic algorithm. As the parallelization solution, the GPU hardware processing on CUDA architecture and CUDA C programming language were adopted. The results achieved were between 35 and 40 times faster than the sequential version of the genetic algorithm.