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Parallel algorithms of the Purcell method for direct solution of linear systems

机译:直接求解线性系统的Purcell方法的并行算法

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In this paper, we first demonstrate that the classical Purcell's vector method when combined with row pivoting yields a consistently small growth factor in comparison to the well-known Gauss elimination method, the Gauss-Jordan method and the Gauss-Huard method with partial pivoting. We then present six parallel algorithms of the Purcell method that may be used for direct solution of linear systems. The algorithms differ in ways of pivoting and load balancing. We recommend algorithms Ⅴ and Ⅵ for their reliability and algorithms Ⅲ and Ⅳ for good load balance if local pivoting is acceptable. Some numerical results are presented.
机译:在本文中,我们首先证明,与著名的高斯消元法,高斯-约旦方法和部分枢轴的高斯-休德方法相比,经典的赛尔向量法与行旋转结合时产生的增长因子一直很小。然后,我们提出了Purcell方法的六个并行算法,这些算法可用于线性系统的直接求解。这些算法在数据透视和负载平衡方面有所不同。如果可以接受局部旋转,我们建议算法Ⅴ和Ⅵ的可靠性,以及算法Ⅲ和Ⅳ的良好负载平衡。给出了一些数值结果。

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