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

职业：通用程序动态自调整框架

• 批准号: 0347260
• 负责人: Craig Zilles
• 金额: $ 41万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-01 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0347260&HistoricalAwards=false
• 关键词: CAREER; Framework; Dynamic; Self; Tuning

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.

职业：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”。这些旋钮通常表示折衷（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，但是对于自动设置这些旋钮的综合方法的工作很少。 本文提出的研究旨在填补这一空白，具体地说，本文提出了一个动态自调优框架，用于优化通用程序的编译。 建议的框架从经验优化框架（如ATLAS）的成功中汲取灵感，将他们的想法应用到无法在安装时进行优化的新环境中。 通过在运行时执行调优，代码可以针对特定的输入数据进行优化（对于非数值程序来说是必需的），但是在非静态工作负载的情况下，在保持低开销和良好性能方面存在挑战。