The science of deriving dense linear algebra algorithms
The science of deriving dense linear algebra algorithms
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推导密集线性代数算法的科学
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
R. Geijn
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文献类型：
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作者：
P. Bientinesi;John A. Gunnels;Margaret E. Myers;E. Quintana;R. Geijn
在本文中，我们提出了一种系统方法，用于推导大量经常遇到的密集线性代数运算的高性能算法族。作为推导的一部分，生成算法正确性的建设性证明。这篇文章的结构合理，可以作为新手的教程。然而，该方法已被证明可以为经过深入研究的线性代数运算产生新的高性能算法，并且对于那些希望生成一流高性能代码的人来说也应该会感兴趣。
In this article we present a systematic approach to the derivation of families of high-performance algorithms for a large set of frequently encountered dense linear algebra operations. As part of the derivation a constructive proof of the correctness of the algorithm is generated. The article is structured so that it can be used as a tutorial for novices. However, the method has been shown to yield new high-performance algorithms for well-studied linear algebra operations and should also be of interest to those who wish to produce best-in-class high-performance codes.