Affinity multiprocessor scheduling considering communications and synchronizations using a Multiobjective Iterated Local Search algorithm

This article studies the affinity scheduling problem in multicore computing systems, considering the minimization of communications and synchronizations. The problem consists in assigning a set of tasks to resources to minimize the overall execution time of the set of tasks and the execution time required to compute the schedule. A Multiobjective Iterated Local Search method is proposed to solve the studied affinity scheduling problem, which considers the different times required for communication and synchronization of tasks executing on different cores of a multicore computer. The experimental evaluation of the proposed scheduling method is performed over realistic instances of the scheduling problem, considering a set of common benchmark applications from the parallel scientific computing field, and a modern multicore platform from National Supercomputing Center, Uruguay. The main results indicate that the proposed multiobjective Iterated Local Search method improves up to 21.6% over the traditional scheduling techniques (a standard Round Robin and a Greedy scheduler)