任意数目节点上密集线性代数分解的数据分布方案

Olivier Beaumont, Jean-Alexandre Collin, Lionel Eyraud-Dubois, Mathieu Vérité
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引用次数: 0

摘要

在本文中,我们考虑将一个密集矩阵的块分布到一组齐次节点上的问题。我们考虑了非对称(LU)和对称(Cholesky)分解的情况。如果节点数P不能写成两个相近数的乘积,那么众所周知的2D块循环(2DBC)分布的效率会显著降低。同样,最近引入的对称块循环(SBC)分布仅对p的特定值有效。在这两种情况下,我们提出了这些分布的推广,以使它们适应任意数量的节点。我们表明,这在理论上和实践中都为现有方案(2DBC和SBC)提供了改进,利用基于任务的运行时系统(如Chameleon和StarPU)带来的灵活性和编程便利性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Distribution Schemes for Dense Linear Algebra Factorizations on Any Number of Nodes
In this paper, we consider the problem of distributing the tiles of a dense matrix onto a set of homogeneous nodes. We consider both the case of non-symmetric (LU) and symmetric (Cholesky) factorizations. The efficiency of the well-known 2D Block-Cyclic (2DBC) distribution degrades significantly if the number of nodes P cannot be written as the product of two close numbers. Similarly, the recently introduced Symmetric Block Cyclic (SBC) distribution is only valid for specific values of P. In both contexts, we propose generalizations of these distributions to adapt them to any number of nodes. We show that this provides improvements to existing schemes (2DBC and SBC) both in theory and in practice, using the flexibility and ease of programming induced by task-based runtime systems like Chameleon and StarPU.
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