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Abstract
In this paper we present a fast parallel Gaussian algorithm for solving a large sparse system on a distributed memory model(DMM) by designing dynamic local pivoting and proper data distribution schemes which sharply reduce the communication time and achieve a balanced distribution of computation load. We shall show that the computation overhead caused by the local pivoting can be compensated by efficient data processing using those two schemes.
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