Improving I/O Complexity of Triangle Enumeration

Cui, Yi; Xiao, Di; Cline, Daren B. H.; Loguinov, Dmitri

doi:10.1109/tkde.2020.3003259

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…This paper focuses on triangle enumerating and our conclusions are also tenable for triangle counting. As all previous works have done, we do not actually output each triangle and just call a function enum with the triangle as its parameter after a triangle is found [18]- [20].…”

Section: A Triangle Computationmentioning

confidence: 99%

“…6 In the real-world graphs, soc-lj, 7 twitter 7 and friendster 8 are social networks; arabic, 9 IRLhost, 10 gsh, 9 uk 9 and eu 9 are web graphs. Orientation can be executed in preprocessing [11], [20], [21], [33] or on the fly [4], [5], [19]. Because the binary code of PTE only accepts graphs with orientation executed, the orientation of all datasets is uniformly done in preprocessing for fair comparisons.…”

Section: ) Datasetsmentioning

confidence: 99%

“…Hu et al [19] present MGT, which buffers a specific number of edges in memory and search all the triangles containing one of the in-memory edges by traversing every vertex. Cui et al [20] propose Trigon, which creates a scheme based on the lessons learned from previous works and allows balance between the I/O and CPU costs in order to achieve high speed.…”

Section: A Single Node Out-of-core Tcmentioning

confidence: 99%

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LiteTE: Lightweight, Communication-Efficient Distributed-Memory Triangle Enumerating

et al. 2019

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Distributed-memory triangle enumerating has attracted considerable interests due to its potential capability to process huge graphs quickly. However, existing algorithms suffer from low speed due to high communication cost and load imbalance. To solve the problems, we propose LiteTE, a lightweight, communication-efficient triangle enumerating scheme. To reduce communication cost, LiteTE proposes several techniques, including a graph partitioning method to fully leverage the large memory of commodity servers and the high bandwidth of modern networks and a fast broadcast algorithm to effectively utilize the bidirectional bandwidth of cables and the aggregate bandwidth of clusters. To reduce load imbalance, LiteTE proposes three-level techniques, including a codesign technique of graph partitioning and partition-level load balance, a decentralized dynamic node-level load balance technique, and a chunk-based lock-free work-stealing technique, all of which are lightweight and incur no or hardly any communication cost. The experimental results show that LiteTE reduces communication cost and load imbalance considerably and achieves much better performance in metrics, such as setup time, runtime, scalability, and load balance than the state-of-the-art algorithms. On a small-scale cluster, LiteTE enumerates the 15 trillion triangles in a graph of 92 billion edges in 15 min, while other algorithms fail to complete. INDEX TERMS Triangle enumerating, triangle computation, graph processing, distributed computing, parallel processing.

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Section: A Triangle Computationmentioning

confidence: 99%