用于多核架构的完全经验自动调整的QR因子分解
Fully Empirical Autotuned QR Factorization For Multicore Architectures
摘要：
随着时间的推移，随着系统变得越来越复杂，调优数值库变得越来越困难。特别是，现代多核机器使得算法的行为难以预测和建模。在本文中，我们解决了在多核架构上调整密集QR分解的问题。我们表明，这是很难依赖于一个模型，这促使我们设计一个完全实证的方法。我们表现出一些强大的经验性质，使我们能够有效地修剪搜索空间。我们的方法是自动的，快速和可靠的。在七个平台中的五个平台上，调优过程在安装时不到一分钟和十分钟就完全完成了。根据平台的不同，我们实现的平均性能从97%到100%不等。这项工作是自动调优PLASMA库和实现跨硬件系统的轻松性能可移植性的基础。
英文摘要：
Tuning numerical libraries has become more difficult over time, as systems get more sophisticated. In particular, modern multicore machines make the behaviour of algorithms hard to forecast and model. In this paper, we tackle the issue of tuning a dense QR factorization on multicore architectures. We show that it is hard to rely on a model, which motivates us to design a fully empirical approach. We exhibit few strong empirical properties that enable us to efficiently prune the search space. Our method is automatic, fast and reliable. The tuning process is indeed fully performed at install time in less than one and ten minutes on five out of seven platforms. We achieve an average performance varying from 97% to 100% of the optimum performance depending on the platform. This work is a basis for autotuning the PLASMA library and enabling easy performance portability across hardware systems.
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