Streaming graph analysis extracts timely insights from evolving graphs, and has gained increasing popularity. In current practice of streaming graph analysis, incoming updates are simply cached in a buffer, until being applied onto existing graph structure to construct a new snapshot. Graph algorithms then work on the new snapshot to produce up-to-date analysis result. Nevertheless, we find that for widely used monotonic graph algorithms, the analysis process can be accelerated by preprocessing buffered updates. To this end, we propose GraPU, a streaming graph analytics system for monotonic graph algorithms. Before applying updates, GraPU preprocesses buffered updates in three consecutive stages: 1) Components-based Classification first identifies the effective graph data that are actually affected by current updates, by classifying the vertices involved in buffered updates according to the predetermined connected components in underlying graph; 2) In-buffer Precomputation generates the safe and profitable intermediate values that can be later merged onto underlying graph to facilitate convergence on new snapshots, by precomputing the values of vertices involved in buffered updates; 3) Hub-vertices Division eliminates the vertex-level load imbalance for analysis on new snapshots, by automatically identifying the high-degree vertices involved in updates and efficiently distributing their high-cost computation over multiple machines. After buffered updates are applied, GraPU calculates vertex values in new snapshots using the subgraph-centric model. GraPU further presents Load-factors Guided Balancing to achieve load balance at subgraph-level, by reassigning some vertices and edges among subgraphs beforehand. Our experimental result shows that, GraPU outperforms state-of-the-art KineoGraph by up to 20.43x.

中文翻译：

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利用缓冲更新进行快速流图分析

流式图分析从不断发展的图中及时提取洞察力，并且越来越受欢迎。在流式图分析的当前实践中，传入的更新只是简单地缓存在缓冲区中，直到应用于现有的图结构以构建新的快照。然后图形算法处理新的快照以产生最新的分析结果。尽管如此，我们发现对于广泛使用的单调图算法，可以通过预处理缓冲更新来加速分析过程。为此，我们提出了 GraPU，一种用于单调图算法的流式图分析系统。在应用更新之前，GraPU 在三个连续阶段对缓冲更新进行预处理：1) 基于组件的分类首先识别实际受当前更新影响的有效图数据，通过根据底层图中预先确定的连通分量对缓冲更新中涉及的顶点进行分类；2) 缓冲区内预计算生成安全且有利可图的中间值，这些中间值可以稍后合并到底层图上，通过预先计算缓冲区更新中涉及的顶点值来促进新快照的收敛；3）Hub-vertices Division 通过自动识别更新中涉及的高度顶点并有效地将其高成本计算分配到多台机器上，消除了用于分析新快照的顶点级负载不平衡。应用缓冲更新后，GraPU 使用以子图为中心的模型计算新快照中的顶点值。GraPU 进一步提出了 Load-factors Guided Balancing 以实现子图级别的负载平衡，通过预先在子图中重新分配一些顶点和边。我们的实验结果表明，GraPU 的性能比最先进的 KineoGraph 高出 20.43 倍。