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A Distributed Memory Parallel Gauss-seidel Algorithm For Linear Algebraic Systems

机译:线性代数系统的分布式内存并行高斯-赛德尔算法

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A distributed memory parallel Gauss-Seidel algorithm for linear algebraic systems is presented, in which a parameter is introduced to adapt the algorithm to different distributed memory parallel architectures. In this algorithm, the coefficient matrix and the right-hand side of the linear algebraic system are first divided into row-blocks in the natural rowwise-order according to the performance of the parallel architecture in use. And then these row-blocks are distributed among local memories of all processors through torus-wrap mapping techniques. The solution iteration vector is cyclically conveyed among processors at each iteration so as to decrease the communication. The algorithm is a true Gauss-Seidel algorithm which maintains the convergence rate of the serial Gauss-Seidel algorithm and allows existing sequential codes to run in a parallel environment with a little investment in recoding. Numerical results are also given which show that the algorithm is of relatively high efficiency.
机译:提出了一种线性代数系统的分布式内存并行Gauss-Seidel算法,引入了一个参数使算法适应不同的分布式内存并行体系结构。在该算法中,首先根据使用的并行体系结构的性能,将系数矩阵和线性代数系统的右侧按自然的行顺序划分为行块。然后,这些行块通过环形包装映射技术分布在所有处理器的本地存储器中。解决方案迭代向量在每次迭代时在处理器之间循环传送,以减少通信。该算法是真正的Gauss-Seidel算法,可以保持串行Gauss-Seidel算法的收敛速度,并允许现有的顺序代码在并行环境中运行,而对重新编码的投资很少。数值结果表明该算法效率较高。

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