稠密线性代数的性能建模
Performance Modeling for Dense Linear Algebra
AI总结：
本文提出一个自动生成BLAS和LAPACK统计性能模型的框架，通过评估和组合这些模型来正确排序等价算法并调优参数，在单核和多核环境下均有效。
摘要：
众所周知，稠密线性代数算法的行为受到目标架构、底层库甚至问题规模等因素的极大影响；因此，准确预测其性能是一个真正的挑战。在本文中，我们并不关注为给定算法创建精确模型，而是根据性能正确排序一组等价算法。意识到稠密线性代数例程的层次结构，我们通过开发一个自动生成BLAS和LAPACK库统计性能模型的框架来处理该问题。这使我们能够通过评估和组合这些模型来获得预测。我们证明了该方法在单核和多核环境中均能成功，不仅在算法排序方面，而且在参数调优方面也是如此。
英文摘要：
It is well known that the behavior of dense linear algebra algorithms is greatly influenced by factors like target architecture, underlying libraries and even problem size; because of this, the accurate prediction of their performance is a real challenge. In this article, we are not interested in creating accurate models for a given algorithm, but in correctly ranking a set of equivalent algorithms according to their performance. Aware of the hierarchical structure of dense linear algebra routines, we approach the problem by developing a framework for the automatic generation of statistical performance models for BLAS and LAPACK libraries. This allows us to obtain predictions through evaluating and combining such models. We demonstrate that our approach is successful in both single- and multi-core environments, not only in the ranking of algorithms but also in tuning their parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
