We introduce XtraPuLP, a distributed-memory graph partitioner designed to process irregular trillion-edge graphs. XtraPuLP is based on the scalable label propagation community detection technique, which has been demonstrated in various prior works as a viable means to produce high quality partitions of skewed and small-world graphs with minimal computation time. Our XtraPuLP implementation can also be generalized to compute partitions with an arbitrary number of constraints, and it can compute partitions with balanced communication load across all parts. On a collection of large sparse graphs, we show that XtraPuLP partitioning is considerably faster than state-of-the-art partitioning methods, while also demonstrating that XtraPuLP can produce partitions of real-world graphs with billion+ vertices and over a hundred billion edges in minutes. Additionally, we demonstrate XtraPuLP on a variety of applications, including large-scale graph analytics and sparse matrix-vector multiplication.

中文翻译：

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可扩展、多约束、复杂目标的图分区

我们介绍了 XtraPuLP，这是一种分布式内存图分区器，旨在处理不规则的万亿边图。XtraPuLP 基于可扩展的标签传播社区检测技术，该技术已在各种先前的工作中被证明是一种可行的方法，可以以最少的计算时间生成倾斜和小世界图的高质量分区。我们的 XtraPuLP 实现也可以推广到计算具有任意数量约束的分区，并且它可以计算所有部分之间具有平衡通信负载的分区。在大型稀疏图的集合上，我们表明 XtraPuLP 分区比最先进的分区方法快得多，同时还证明 XtraPuLP 可以生成具有 10 亿个以上顶点和超过 1000 亿条边的真实世界图的分区。分钟。