Parallel approaches of genetic algorithm in the MIC architecture of the Intel Xeon Phi

Abstract

Today, genetic algorithms are widely used in many fields such as bioinformatics, computer science, artificial intelligence, finance ... Genetic algorithms are applied to create high quality solutions for complex optimization problems in the above industries. There have been many studies based on the proposed new hardware architecture that aims to speed up the execution of genetic algorithms as quickly as possible. Some studies suggest parallel genetic algorithms on systems with multicore CPUs and / or graphics processing units (GPUs). However, very few solutions propose a genetic algorithm that can be run on systems that use the new Intel Xeon Phi co-processor (Intel Many-Integrated Core (MIC) architecture). For that reason, we propose and develop the study of the genetic algorithm on high-performance computing systems with Intel Xeon Phi co-processors. This study will present the results of parallel approaches of genetic algorithm on one and more Intel Xeon Phi co-processors by the following methods: (i) Intel Xeon Phi programming model Offload and Native; and (ii) a combined model of MPI and OpenMP. The proposed genetic algorithm can find the optimal schedule for the energy-efficient scheduling problem of virtual machines on physical machines with the goal of minimization total energy consumption. The results of the simulations show the feasibility of implementing a genetic algorithm on one or many Intel Xeon Phi. Genetic algorithm on one or more distributed Intel Xeon Phi always results in faster algorithm execution time than sequential genetic algorithm and the ability to find better solutions using more Intel Xeon Phi. This research result can be applied to other meta-heuristic like TABU search, Ant Colony Optimization.