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Pregel: A System for Large-Scale Graph Processing

机译:Pregel:用于大规模图形处理的系统

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Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs in some cases billions of vertices, trillions of edges-poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible enough to express a, broad set of algorithms. The model has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronic-ity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.
机译:许多实用的计算问题涉及大图。标准示例包括Web图形和各种社交网络。在某些情况下,这些图表的比例在数十亿个顶点,数万亿的边缘 - 对其有效处理构成挑战。在本文中,我们提出了一种适合此任务的计算模型。程序被表示为迭代序列,在每个顶点可以接收在先前迭代中发送的消息,向其他顶点发送消息,并修改其自己的状态和传出边缘或变异图形拓扑。这种以顶点为中心的方法足够灵活,以表达一组广泛的算法。该模型专为有效,可扩展性和容错容易实现,在数千种商品计算机的集群上,其隐含的同步型ITY使得有关程序的推理更容易。分发相关的详细信息隐藏在抽象API后面。结果是处理表现力和易于编程的大图的框架。

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