Efficient graph partitioning is vital for high-performance graph-parallel systems. Traditional graph partitioning methods attempt to both minimize communication cost and guarantee load balancing in computation. However, the skewed degree distribution of natural graphs makes it difficult to simultaneously achieve the two objectives. This article proposes topology refactorization (TR), a topology-aware method allowing graph-parallel systems to separately handle the two objectives: refactorization is mainly focused on reducing communication cost, and partitioning is mainly targeted for balancing the load. TR transforms a skewed graph into a more communication-efficient topology through fusion and fission, where the fusion operation organizes a set of neighboring low-degree vertices into a super-vertex, and the fission operation splits a high-degree vertex into a set of sibling sub-vertices. Based on TR, we design an efficient graph-parallel system (TopoX) which pipelines refactorization with partitioning to both reduce communication cost and balance computation load. Prototype evaluation shows that TopoX outperforms PowerLyra by up to 78.5% (from 37.2%) on real-world graphs and is faster than most other graph-parallel systems, while only introducing small overhead and memory consumption.

中文翻译：

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TopoX：拓扑重构以最小化图计算中的网络通信


高效的图分区对于高性能图并行系统至关重要。传统的图划分方法试图最小化通信成本并保证计算中的负载平衡。然而，自然图的倾斜度分布使得很难同时实现这两个目标。本文提出了拓扑重构（TR），这是一种拓扑感知方法，允许图并行系统分别处理两个目标：重构主要针对降低通信成本，分区主要针对平衡负载。 TR通过融合和裂变将倾斜图转变为通信效率更高的拓扑，其中融合操作将一组相邻的低度顶点组织成超级顶点，裂变操作将高度顶点分裂成一组同级子顶点。基于TR，我们设计了一个高效的图并行系统（TopoX），该系统通过分区进行管道重构，以减少通信成本并平衡计算负载。原型评估表明，TopoX 在现实世界图上的性能比 PowerLyra 高出 78.5%（之前为 37.2%），并且比大多数其他图并行系统更快，同时仅引入很小的开销和内存消耗。