CAREER: A Framework for Dynamic Self-Tuning of General Purpose Programs

职业：通用程序动态自调优框架摘要随着计算系统变得越来越复杂，它们暴露出越来越多的可用于调优的“旋钮”。这些旋钮通常代表一种权衡（例如，本地数据存储资源的大小与速度），因此必须针对不同的工作负载进行不同的设置，以实现最佳性能（或功率性能）。虽然已经提出了许多这样的旋钮，但很少有关于自动设置这些旋钮的综合方法的工作。拟议中的研究旨在帮助填补这一空白。具体来说，本工作提出了一个动态自调优框架，用于优化通用程序的编译。提出的框架从ATLAS等经验优化框架的成功中汲取灵感，将其思想应用于无法在安装时进行优化的新环境。通过在运行时执行调优，可以针对特定的输入数据对代码进行优化（对于非数字程序来说是必要的），但是在非固定工作负载上保持低开销和良好性能带来了挑战。

CAREER: A Framework for Dynamic Self-Tuning of General Purpose ProgramsAbstractAs computing systems become more complicated, they are exposing an increasingly large number of "knobs" that can be used for tuning. These knobs typically represent a trade-off (e.g., size vs. speed of a local data storage resource) and thus must be set differently for different workloads to achieve optimal performance (or power-performance). While many such knobs have been proposed, there has been little work towards a comprehensive approach to set these knobs automatically. The proposed research is meant to help fill this gap.Specifically, this work proposes a dynamic self-tuning framework for optimizing the compilation of general-purpose programs. The proposed framework draws inspiration from the successes in empirical optimization frameworks like ATLAS, applying their ideas to a new context where optimization cannot be done at install time. By performing the tuning at run time, code can be optimized for specific input data (a necessity for non-numeric programs), but introduces challenges in maintaining low overhead and good performance in the presence on non-stationary workloads.