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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Scalable, Multi-Constraint, Complex-Objective Graph Partitioning
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Scalable, Multi-Constraint, Complex-Objective Graph Partitioning

机译：可扩展，多约束，复杂目标图分隔

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摘要

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.
机译：我们介绍了Xtrapulp，一个分布式内存图形分区器，旨在处理不规则的万亿边图。 Xtrapulp基于可伸缩标签传播社区检测技术，该群体检测技术已在各种先前作用中证明是可行的方法，以产生具有最小计算时间的偏斜和小世界图形的高质量分区。我们的Xtrapulp实现也可以推广以计算具有任意数量的约束的分区，并且可以计算具有跨所有部分的平衡通信负载的分区。在一个大稀疏图形的集合上，我们表明Xtrapulp分区比最先进的分区方法快得多，同时也证明Xtrapulp可以产生亿万+顶点的现实世界图表的分区，并且超过千亿边缘分钟。此外，我们在各种应用程序上展示了Xtrapulp，包括大规模的图形分析和稀疏矩阵矢量乘法。

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