An Efficient Load-Balancing Processor Scheduling Algorithm for Parallelization of Gauss-Seidel Type Algorithms

Abstract

The paper extends our earlier results on the parallelization of Gauss-Seidel (G-S) algorithms for power flow analysis. In the earlier paper, we formulate the parallelizing process as a basic coloring problem, which satisfies the constraint that no directly connected vertices have the same color, without worrying about the constraint on the number of available processors. In this paper, this extra constraint is considered. A heuristic approach is developed to maximize the processor efficiency under the number of processor constraint. The idea is to fully utilize the processor resource, to balance the computational load, and to maximize the use of newly computed data for faster convergence. Some examples and test results are described in this paper.