This article proposes a coded distributed graph processing framework to alleviate the communication bottleneck in large-scale distributed graph processing. In particular, we propose a topology-aware coded computing (TACC) algorithm that has two novel salient features: (i) a topology-aware graph allocation strategy, and (ii) a coded aggregation scheme that combines the intermediate computations for graph processing while constructing coded messages. The proposed setup results in a trade-off between computation and communication, in that increasing the computation load at the distributed parties can in turn reduce the communication load. We demonstrate the effectiveness of the TACC algorithm by comparing the communication load with existing setups on both Erdös-Rényi and Barabási-Albert type random graphs, as well as real-world Google web graph for PageRank computations. In particular, we show that the proposed coding strategy can lead to up to $82\%$ reduction in communication load and up to $46\%$ reduction in overall execution time, when compared to the state-of-the-art and implemented on the Amazon EC2 cloud compute platform.

中文翻译：

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TACC：分布式图形处理的拓扑感知编码计算

本文提出了一种编码的分布式图形处理框架，以减轻大规模分布式图形处理中的通信瓶颈。特别是，我们提出了一种拓扑感知的编码计算（TACC）算法，该算法具有两个新颖的显着特征：（i）一种拓扑感知的图分配策略，以及（ii）结合了中间计算以进行图处理的编码聚合方案，而构造编码消息。所提出的设置导致计算和通信之间的折衷，因为增加分布式方的计算负荷可以进而减少通信负荷。通过将通信负载与Erdös-Rényi和Barabási-Albert型随机图上的现有设置进行比较，我们证明了TACC算法的有效性，以及用于PageRank计算的真实Google Web图形。特别是，我们表明，提出的编码策略可以导致$ 82 \％$ 减少通讯负荷 $ 46 \％$ 与最新技术相比，并在Amazon EC2云计算平台上实施，可减少总体执行时间。