通过图排序加速图处理

Hao Wei, J. Yu, Can Lu, Xuemin Lin
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引用次数: 132

摘要

CPU缓存性能是影响数据库系统效率的关键问题之一。据报道,在数据库系统中,缓存丢失延迟占用了一半的执行时间。为了提高CPU缓存性能,有研究支持搜索,包括缓存无关树和缓存意识树。在本文中,我们通过降低不同图算法的CPU缓存缺失率来关注图计算的CPU加速。处理树的方法不适用于本质上复杂的图。在本文中,我们探索了一种加速CPU计算的通用方法,以便在不改变图算法(实现)和所使用的数据结构的情况下进一步提高图算法的效率。也就是说,我们的目标是设计一个通用的解决方案,既不是针对特定的图算法,也不是针对特定的数据结构。本文研究的方法是图排序,即通过将频繁访问的节点集中在局部,找到给定图中所有节点的最优排列,以最小化CPU缓存缺失率。证明了图的排序问题是np困难的,并给出了一个有界逼近的基本算法。为了提高基本算法的时间复杂度,我们进一步提出了一种基于新数据结构的新的优化技术来降低时间复杂度和提高效率的新算法。我们进行了大量的实验来评估我们的方法,并与其他9种可能的图排序(例如METIS获得的图排序)进行比较,使用8个大型真实图和9个代表性图算法。我们确认我们的方法可以通过降低CPU缓存缺失率来实现高性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speedup Graph Processing by Graph Ordering
The CPU cache performance is one of the key issues to efficiency in database systems. It is reported that cache miss latency takes a half of the execution time in database systems. To improve the CPU cache performance, there are studies to support searching including cache-oblivious, and cache-conscious trees. In this paper, we focus on CPU speedup for graph computing in general by reducing the CPU cache miss ratio for different graph algorithms. The approaches dealing with trees are not applicable to graphs which are complex in nature. In this paper, we explore a general approach to speed up CPU computing, in order to further enhance the efficiency of the graph algorithms without changing the graph algorithms (implementations) and the data structures used. That is, we aim at designing a general solution that is not for a specific graph algorithm, neither for a specific data structure. The approach studied in this work is graph ordering, which is to find the optimal permutation among all nodes in a given graph by keeping nodes that will be frequently accessed together locally, to minimize the CPU cache miss ratio. We prove the graph ordering problem is NP-hard, and give a basic algorithm with a bounded approximation. To improve the time complexity of the basic algorithm, we further propose a new algorithm to reduce the time complexity and improve the efficiency with new optimization techniques based on a new data structure. We conducted extensive experiments to evaluate our approach in comparison with other 9 possible graph orderings (such as the one obtained by METIS) using 8 large real graphs and 9 representative graph algorithms. We confirm that our approach can achieve high performance by reducing the CPU cache miss ratios.
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