Properties of simulated annealing and genetic algorithms for mapping data to multicomputers

Abstract

We experimentally analyze some properties of simulated annealing algorithms (SA) and genetic algorithms (GA) for mapping data to multicomputers. These properties include sensitiviiy to user parameters, fault tolerance capability, and applicability to different multicomputer topologies. Some user parameters are included in the objective function and are architecture- or problem-dependent parameters. The others are used in the GA and SA algorithms. The fault tolerance capability is demonstrated by mapping data to a multicomputer with some faulty processors. We assume a hypercube multicomputer architecture in most experiments. However, mapping to mesh, array, ring, tree, and star graph topologies is also demonstrated. The experimental results show that the GA and SA are insensitive to user parameters in wide ranges, completely fault tolerant, and unbiased towards particular multicomputer topologies. These properties of flexibility and general applicability, which are lacking in other heuristic algorithms, make the GA and SA attractive for automatic parallelization systems.