Task allocation in mesh connected processors with local search meta-heuristic algorithms

Abstract

This article contains a short analysis of applying three metaheuristic local search algorithms to solve the problem of allocating two-dimensional tasks on a two-dimensional processor mesh in a period of time. The primary goal is to maximize the level of mesh utilization. To achieve this task we adapted three algorithms: Tabu Search, Simulated Annealing and Random Search, as well as created a helper algorithm Dumb Fit and adapted another helper algorithm - First Fit. To measure the algorithms' efficiency we introduced our own evaluating function Cumulative Effectiveness and a derivative Utilization Factor. Finally, we implemented an experimentation system to test these algorithms on different sets of tasks to allocate. In this article there is a short analysis of series of experiments conducted on three different classes of task sets: small tasks, mixed tasks and large tasks. © 2010 Springer-Verlag Berlin Heidelberg.