使用数据移动距离(DMD)测量缓存复杂度

Donovan Snyder, C. Ding
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引用次数: 1

摘要

鉴于基于缓存的机器无处不在,分析程序或算法如何使用缓存是很重要的。目前还没有一种被广泛接受的缓存复杂性度量方法,但是对于性能来说,缓存复杂性通常比时间和空间复杂性度量更重要。本文提出了数据移动距离(DMD)来衡量算法缓存复杂度的代价,演示了它的使用,并讨论了它作为局部性的度量。由于处理器的速度越来越快,现代计算的主要瓶颈之一是将所需的数据移入处理器或在处理器周围移动。DMD从这个意义上衡量算法的效率,因此可能是对传统计算复杂性分析的急需补充。本文对DMD进行了综述,并给出了一些基本结果。这些将在今后的工作中加以扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring Cache Complexity Using Data Movement Distance (DMD)
Given the ubiquity of cache-based machines, it is important to analyze how well a program or an algorithm uses the cache. There is no widely accepted measure of cache complexity, yet the cache complexity is often more important to performance than the measures of time and space complexity. This paper presents Data Movement Distance (DMD) to measure the cost of cache complexity for algorithms, demonstrates its use, and discusses it as a measure of locality. Since processor speeds are getting ever faster, one of the main bottlenecks in modern computing is moving the needed data into and around the processor. DMD measures the efficiency of the algorithm in this sense and therefore may be a much-needed complement to the conventional analysis of computation complexity. In this paper, we give an overview of DMD and some basic results. These will be expanded upon in future work.
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