This article presents GRAPE, a parallel <underline>GRAP</underline>h <underline>E</underline>ngine for graph computations. GRAPE differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole, without the need for recasting the entire algorithm into a new model. Underlying GRAPE are a simple programming model and a principled approach based on fixpoint computation that starts with partial evaluation and uses an incremental function as the intermediate consequence operator. We show that users can devise existing sequential graph algorithms with minor additions, and GRAPE parallelizes the computation. Under a monotonic condition, the GRAPE parallelization guarantees to converge at correct answers as long as the sequential algorithms are correct. Moreover, we show that algorithms in MapReduce, BSP, and PRAM can be optimally simulated on GRAPE. In addition to the ease of programming, we experimentally verify that GRAPE achieves comparable performance to the state-of-the-art graph systems using real-life and synthetic graphs.

中文翻译：

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并行化顺序图计算

本文介绍 GRAPE，一种用于图形计算的并行 <underline>GRAP</underline>h <underline>E</underline>引擎。GRAPE 与现有系统的不同之处在于它能够将现有的顺序图算法作为一个整体进行并行化，而无需将整个算法重新转换为新模型。GRAPE 的基础是一个简单的编程模型和一种基于定点计算的原则性方法，该方法从部分评估开始，并使用增量函数作为中间结果运算符。我们展示了用户可以通过少量添加来设计现有的顺序图算法，并且 GRAPE 使计算并行化。在单调条件下，只要顺序算法是正确的，GRAPE 并行化就保证收敛到正确的答案。而且，我们展示了 MapReduce、BSP 和 PRAM 中的算法可以在 GRAPE 上进行最佳模拟。除了易于编程之外，我们还通过实验验证了 GRAPE 使用真实和合成图实现了与最先进的图系统相当的性能。