简单缓存模型在现代处理器中的适用性

Rakhi Hemani, Subhasish Banerjee, Apala Guha
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引用次数: 1

摘要

缓存性能评估是确保良好的缓存利用率和应用程序性能的第一步。然而,由于商业缓存的实现是复杂的、不断发展的和受保护的信息,因此很难创建良好的缓存模型。因此,许多实际的编译器使用简单的缓存模型,如完全关联LRU缓存(FALC)模型。在本文中,我们量化了三种现代处理器的FALC模型的适用性。我们的调查显示,适用性既依赖于应用程序,也依赖于体系结构。这种洞察力用于开发早期(即不需要概要分析)适用性识别的模型:早期挑选标准。早期挑选标准是使用合成基准开发的,并通过15个内存密集型SPEC CPU2006基准进行了验证。通过早期采摘标准确定的所有应用程序都具有很高的适用性。
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 applicability of simple cache models for modern processors
Cache performance estimation is the first step in assuring good cache utilization and hence application performance. However, it is difficult to create good cache models as the implementation of commercial caches is complex, constantly evolving, and, protected information. As a result many practical compilers use simple cache models such as Fully Associative LRU Cache (FALC) model. In this paper we quantify the applicability of the FALC model for three modern processors. Our investigation reveals that the applicability is both application and architecture dependent. This insight is used to develop a model for an early (i.e. no profiling required) identification of applicability: Early Picking Criterion. The Early Picking Criterion is developed using synthetic benchmarks and validated with 15 memory intensive SPEC CPU2006 benchmarks. All applications identified by the Early Picking Criterion demonstrate high applicability.
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