A Parallel Genetic-Search-Based Algorithm for Scheduling Arbitrary Task Graphs to Multiprocessors

Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. In this paper we propose a novel GA-based algorithm with an objective to simultaneously meet the goals of high performance, scalability, and fast running time. The proposed PGS (Parallel Genetic Scheduling) algorithm itself is a parallel algorithm and generates high quality solutions in a short time. By encoding a scheduling list as a chromosome, the PGS algorithm can potentially generate an optimal scheduling list which in turn leads to an optimal schedule. The major strength of the PGS algorithm lies in its two efficient genetic operators: the order crossover and mutation which effectively combine the building-blocks of good scheduling lists to construct better lists. When evaluated with random task graphs for which optimal solutions are known, the algorithm generates optimal solutions for more than half of the test cases and close-to-optimal for the remaining cases.