Ingress: an automated incremental graph processing system

Shufeng Gong, Chao Tian, Qiang Yin, Zhengdong Wang, Song Yu, Yanfeng Zhang, Wenyuan Yu, Liang Geng, Chong Fu, Ge Yu, Jingren Zhou
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Abstract

The graph data keep growing over time in real life. The ever-growing amount of dynamic graph data demands efficient techniques of incremental graph computation. However, incremental graph algorithms are challenging to develop. Existing approaches usually require users to manually design nontrivial incremental operators, or choose different memoization strategies for certain specific types of computation, limiting the usability and generality. In light of these challenges, we propose \(\textsf{Ingress}\), an automated system for incremental graph proc essing. \(\textsf{Ingress}\) is able to deduce the incremental counterpart of a batch vertex-centric algorithm, without the need of redesigned logic or data structures from users. Underlying \(\textsf{Ingress}\) is an automated incrementalization framework equipped with four different memoization policies, to support all kinds of vertex-centric computations with optimized memory utilization. We identify sufficient conditions for the applicability of these policies. \(\textsf{Ingress}\) chooses the best-fit policy for a given algorithm automatically by verifying these conditions. In addition to the ease-of-use and generalization, \(\textsf{Ingress}\) outperforms state-of-the-art incremental graph systems by \(12.14\times \) on average (up to \(49.23\times \)) in efficiency.

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Ingress:自动增量图处理系统
在现实生活中,图形数据会随着时间不断增长。不断增长的动态图数据量需要高效的增量图计算技术。然而,增量图算法的开发极具挑战性。现有方法通常要求用户手动设计非难增量算子,或为某些特定类型的计算选择不同的记忆化策略,从而限制了其可用性和通用性。鉴于这些挑战,我们提出了增量图处理自动化系统(textsf{Ingress}\)。\它能够推导出以顶点为中心的批量算法的增量对应算法,而不需要用户重新设计逻辑或数据结构。\textsf{Ingress}\)的基础是一个自动增量框架,它配备了四种不同的内存化策略,可以支持各种以顶点为中心的计算,并优化内存利用率。我们确定了这些策略适用性的充分条件。\textsf{Ingress}\)通过验证这些条件,自动为给定算法选择最合适的策略。除了易用性和通用性之外,\(\textsf{Ingress}\)在效率上平均比最先进的增量图系统高出(12.14\times\)(最高可达(49.23\times\))。
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