| Abstract: | We present a parallel algorithm for distributed memory multiprocessors, which is based on generalized marching (GM), one of the fastest methods in the class of fast Poisson solvers. The GM algorithm is not suited for any but very coarse-grain parallel processing. The main difficulty with parallelization is that the number of independent processes and the amount of work in each process change exponentially and in inverse proportion of each other. To improve parallelism, the matrices involved in GM are diagonalized performing multiple FFTs. In this way, independent processes extending across all the algorithm are obtained. The parallel GM has been tested on an Ncube/10 and a Symult S2010, running the Express communication system. A performance evaluation has been carried out using a scaled efficiency model and some classical parameters. |
