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arXiv · 1610.07220

Partitioning Trillion-edge Graphs in Minutes

George M Slota·Sivasankaran Rajamanickam·Karen Devine·Kamesh Madduri
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Abstract

We introduce XtraPuLP, a new distributed-memory graph partitioner designed to process trillion-edge graphs. XtraPuLP is based on the scalable label propagation community detection technique, which has been demonstrated as a viable means to produce high quality partitions with minimal computation time. On a collection of large sparse graphs, we show that XtraPuLP partitioning quality is comparable to state-of-the-art partitioning methods. We also demonstrate that XtraPuLP can produce partitions of real-world graphs with billion+ vertices in minutes. Further, we show that using XtraPuLP partitions for distributed-memory graph analytics leads to significant end-to-end execution time reduction.

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George M Slota, Sivasankaran Rajamanickam, Karen Devine, Kamesh Madduri. 2016-10-23. Partitioning Trillion-edge Graphs in Minutes. https://arxiv.org/abs/1610.07220

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