Allocating data to distributed-memory multiprocessors by genetic algorithms

Abstract

We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. These are a sequential hybrid GA, a coarse-grain GA and a fine-grain GA. The last two are based on models of natural evolution that are suitable for parallel implementation; they have been implemented on a hypercube and a Connection Machine. Experimental results show that the three GAs evolve good suboptimal solutions which are better than those produced by other methods. The GAs are also robust and do not show a bias towards particular problem configurations. The two parallel GAs have reasonable execution times, with the coarse-grain GA producing better solutions for the allocation of loosely synchronous computations.