Suboptimal Mapping Research Articles

A randomized heuristics for the mapping problem: The genetic approach

The combinatorial optimization problem of assigning parallel tasks onto a multiprocessor so as to minimize execution time is termed as the mapping problem. This problem even in its simplest form is known to NP-hard. Several heuristic solutions that have been proposed seek to obtain a ‘good’ sub-optimal mapping reasonable time. In this paper we present a randomized heuristic for the mapping problem which is based the principles of genetic algorithms. The adaption of the genetic search strategy to this problem and implementation has been discussed. We empirically compare the performance of our randomized map algorithm with an existing random start algorithm based on the recursive min-cut partitioning heuristic. results indicate that the genetic algorithm for mapping is a promising alternative in the domain of random heuristics.