Managing the bottlenecks in parallel Gauss-Seidel type algorithms for power flow analysis
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In our earlier papers, the parallelization and implementations of Gauss-Seidel (G-S) algorithms for power flow analysis have been investigated on a Sequent Balance shared memory (SM) machine. In this paper, we generalize the idea to more general computer architectures and demonstrate how to effectively increase the speedup upper bounds of G-S algorithms by properly managing the bottlenecks on both Sequent Balance SM and nCUBE2 distributed memory (DM) machines. For G-S algorithms, when our coloring process is used to schedule the processors, there is almost no sequential portion. Thus, the only decisive factor left, which has a direct impact on the speedup upper bound, is the synchronization overhead. Accordingly, we propose a new synchronization scheme which can reduce the synchronization overhead on the Sequent Balance machine. Also, on the CUBE2 machine, a new clustered G-S algorithm is proposed and implemented. The algorithm carefully schedules their processors, computational loads and communication overheads for the best performance. In addition, the synchronization overheads and speedup upper bounds on both machines are analyzed in terms of power system size and number of processors. The competitiveness of G-S type algorithms is also discussed. 1993 IEEE
