On Efficient External-Memory Triangle Listing

Cui, Yi; Xiao, Di; Loguinov, Dmitri

doi:10.1109/icdm.2016.0021

Cited by 12 publications

(5 citation statements)

References 31 publications

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“…Compared to truss decomposition, triangle counting is a very well-studied problem. We refer readers to [26], [27], [28], [29], [30], [31], [32] for a sampling of efficient algorithms and practical high-performance implementations. Xiao et al [31] unify a large body of previously-developed triangle counting algorithms and observe that the ordering of vertices and orientation of edges has a significant impact on performance.…”

Section: Introductionmentioning

confidence: 99%

Shared-Memory Graph Truss Decomposition

Kabir

Madduri²

2017

2017 IEEE 24th International Conference on High Performance Computing (HiPC)

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We present PKT, a new shared-memory parallel algorithm and OpenMP implementation for the truss decomposition of large sparse graphs. A k-truss is a dense subgraph definition that can be considered a relaxation of a clique. Truss decomposition refers to a partitioning of all the edges in the graph based on their k-truss membership. The truss decomposition of a graph has many applications. We show that our new approach PKT consistently outperforms other truss decomposition approaches for a collection of large sparse graphs and on a 24-core shared-memory server. PKT is based on a recently proposed algorithm for k-core decomposition.

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Section: Introductionmentioning

confidence: 99%

Shared-Memory Graph Truss Decomposition

Kabir

Madduri²

2017

2017 IEEE 24th International Conference on High Performance Computing (HiPC)

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“…Most recent TC algorithms leverage an efficient degree-based orientation heuristics [4], [5], [8], [11], [12], [15], [16], [18], [21], which is based on a total order ≺ of vertices and defined as follows [18]:…”

Section: B Orientation Heuristicsmentioning

confidence: 99%

“…The key operation in most recent TC algorithms including Surrogate is intersection [4], [5], [8]- [12], [14]- [16], [18], [21]. For each edge (u, v), all triangles containing the edge will be found by doing N u ∩ N v , as illustrated in Fig.…”

Section: Surrogate and Message Inversionmentioning

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%

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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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“…The efforts of seeking suitable hardware platforms to accelerate triangle counting has also gain momentum. Popular attempts include multi-core CPUs [17], [18], [19], [20], manycore GPUs [21], [22], [23], [24], [25], and external memory devices [26], [27], [28], [29], [30]. Of all these platforms, GPUs are particularly tempting for the following reasons.…”

Section: Introductionmentioning

confidence: 99%

TRUST: Triangle Counting Reloaded on GPUs

Pandey,

Wang,

Zhong

et al. 2021

Preprint

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Triangle counting is a building block for a wide range of graph applications. Traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle counting beats vertex-centric design, and iii) communication-free and workload balanced graph partitioning is a grand challenge for triangle counting. On the contrary, we advocate that i) hashing can help the key operations for scalable triangle counting on Graphics Processing Units (GPUs), i.e., list intersection and graph partitioning, ii) vertex-centric design reduces both hash table construction cost and memory consumption, which is limited on GPUs. In addition, iii) we exploit graph and workload collaborative, and hashing-based 2D partitioning to scale vertex-centric triangle counting over 1,000 GPUs with sustained scalability. In this work, we present TRUST which performs triangle counting with the hash operation and vertex-centric mechanism at the core. To the best of our knowledge, TRUST is the first work that achieves over one trillion Traversed Edges Per Second (TEPS) rate for triangle counting.

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On Efficient External-Memory Triangle Listing

Cited by 12 publications

References 31 publications

Shared-Memory Graph Truss Decomposition

Shared-Memory Graph Truss Decomposition

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

TRUST: Triangle Counting Reloaded on GPUs

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