|Autors: Nikolova, I. N., Zapryanov, G. S., Hristov, A., Kimovski, D., Kumbaroska, V.|
Title: Resource management optimization in multi-processor platforms
Keywords: High-Performance Computing, Parallel Programming Model, Parallel Performance, Parallel Algorithm
Abstract: The modern high-performance computing systems (HPCS) are composed of hundreds of thousand computational nodes. An effective resource allocation in HPCS is a subject for many scientific research investigations. Many programming models for effective resources allocation have been proposed. The main purpose of those models is to increase the parallel performance of the HPCS. This paper investigates the efficiency of a parallel algorithm for resource management optimization based on the Artificial Bee Colony (ABC) metaheuristic while solving a package of NP-complete problems on a multi-processor platform. In order to achieve minimal parallelization overhead in each cluster node, a multi-level hybrid programming model is proposed that combines coarse-grain and fine-grain parallelism.
1. Khan I., Meena A., Richhariya P., Dewangan B.K. (2021) Optimization in Autonomic Computing and Resource Management. In: Choudhury T., Dewangan B.K., Tomar R., Singh B.K., Toe T.T., Nhu N.G. (eds) Autonomic Computing in Cloud Resource Management in Industry 4.0. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-71756-8_9 - 2021 - в издания, индексирани в Scopus или Web of Science
Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Google Scholar