TLB misses: The Missing Issue of Adaptive Radix Tree?

Petrie Wong, Ziqiang Feng, Wenjian Xu, Eric Lo, B. Kao
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引用次数: 5

Abstract

Efficient main-memory index structures are crucial to main-memory database systems. Adaptive Radix Tree (ART) is the most recent in-memory index structure. ART is designed to avoid cache miss, leverage SIMD data parallelism, minimize branch mis-prediction, and have small memory footprint. When an in-memory index structure like ART has significantly few cache misses and branch mis-predictions, it is natural to question whether misses in Translation Lookaside Buffer (TLB) matters. In this paper, we try to confirm whether this is the case and if the answer is positive, what are the measures that we can take to alleviate that and how effective they are.
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TLB缺失:自适应基树缺失的问题?
高效的主存索引结构对主存数据库系统至关重要。自适应基树(ART)是最新的内存索引结构。ART旨在避免缓存丢失,利用SIMD数据并行性,最大限度地减少分支错误预测,并具有较小的内存占用。当像ART这样的内存索引结构的缓存缺失和分支错误预测非常少时,很自然地会质疑翻译Lookaside Buffer (TLB)中的缺失是否重要。在本文中,我们试图确认情况是否如此,如果答案是肯定的,我们可以采取哪些措施来缓解这种情况,以及这些措施的效果如何。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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