A Numerical Comparative Analysis of Partitioning Heuristics for Scheduling Task Graphs on Multiprocessors

Many algorithms for scheduling DAGs on multiprocessors have been proposed, but there has been little work done to determine their eeectiveness. Since multi-processor scheduling is an NP-hard problem, no exact tractible algorithm exists, and no baseline is available from which to compare the resulting schedules. Furthermore, performance guarantees have been found for only a few simple DAGs. This paper is an attempt to quantify the diierences in a few of the heuristics. New classiication criteria are deened for the DAGs, and the diierences between the heuristics are noted for various criteria. The comparison is made between a graph based method, and two critical path methods. The empirical performance of the three heuristics is compared when they are applied to the randomly generated DAGs. Abstract Many algorithms to schedule DAGs on multiprocessors have been proposed, but there has been little work done to determine their eeectiveness. Since multi-processor scheduling is an NP-hard problem, no exact tractible algorithm exists, and no baseline is available from which to compare the resulting schedules. Furthermore, performance guarantees have been found for only a few simple DAGs. This paper is an attempt to quantify the diierences in a few of the heuristics. New classiication criteria are deened for the DAGs, and the diierences between the heuristics are noted for various criteria. The comparison is made between a graph based method, and two critical path methods. The empirical performance of the three heuristics is compared when they are applied to the randomly generated DAGs.