Abstract
We consider the problem of mapping processes onto computing nodes so as to reduce the execution time by minimizing communication delays. Our approach relies on a genetic algorithm implementation of the local neighborhood search (LNS) approach and is called Genetic-LNS or GLNS. We also present our parallel version of the GLNS algorithm, called parallel genetic local neighborhood search (P-GLNS). LNS, GNLS, and P-GLNS were implemented and compared. Simulations demonstrate that the GLNS algorithm has better performance than LNS, and that, when the workload is sufficiently high, the P-GLNS algorithm achieved near linear scalability.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
