关于时间数据参考剖面的稳定性

Trishul M. Chilimbi
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引用次数: 24

摘要

不断增长的计算机系统复杂性使得仅仅基于静态分析的程序优化变得越来越困难。因此;许多代码优化都包含来自程序执行配置文件的信息。大多数内存系统优化更进一步,主要依赖于配置文件。这种对概要文件的依赖使得离线优化的有效性依赖于跨多个程序运行的概要文件稳定性。虽然代码配置文件(如基本块、边缘和分支配置文件)已被证明可以满足这一要求,但数据引用配置文件的稳定性,特别是缓存级优化所必需的时间数据引用配置文件,既没有得到研究,也没有得到建立。研究表明,用热数据流(即频繁重复的数据引用序列)表示的时态数据参考轮廓是相当稳定的;一个令人鼓舞的内存优化研究结果。大多数热数据流属于以下两类之一:那些在多次运行中以相同顺序引用其数据元素的数据流,以及那些以不同顺序引用相同元素集的数据流,并且这种类别成员关系非常稳定。此外,属于第一类的热数据流的比例相当大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the stability of temporal data reference profiles
Growing computer system complexity has made program optimization based solely on static analyses increasingly difficult. Consequently; many code optimizations incorporate information from program execution profiles. Most memory system optimizations go further and rely primarily on profiles. This reliance on profiles makes off-line optimization effectiveness dependent on profile stability across multiple program runs. While code profiles such as basic block, edge, and branch profiles, have been shown to satisfy this requirement, the stability of data reference profiles, especially temporal data reference profiles that are necessary for cache level optimizations, has neither been studied nor established. This paper shows that temporal data reference profiles expressed in terms of hot data streams, which are data reference sequences that frequently repeat, are quite stable; an encouraging result for memory optimization research. Most hot data streams belong to one of two categories: those that appear in multiple runs with their data elements referenced in the same order, and those with the same set of elements referenced in a different order, and this category membership is extremely stable. In addition, the fraction of hot data streams that belong to the first category is quite large.
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