Fast BFS-Based Triangle Counting on GPUs

Wang, Leyuan; Owens, John D.

doi:10.1109/hpec.2019.8916434

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…And performance-wise, this implementation compares poorly to triangle-matching-specific algorithms. Their recent update to their implementation [17] for the triangle-counting GraphChallenge outperforms the previous year's champion; our work here extends their approach to generalized subgraphs.…”

Section: Gpu-based Subgraph Matchingmentioning

confidence: 83%

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Fast Gunrock Subgraph Matching (GSM) on GPUs

Wang,

Owens

2020

Preprint

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In this paper, we propose a GPU-efficient subgraph isomorphism algorithm using the Gunrock graph analytic framework, GSM (Gunrock Subgraph Matching), to compute graph matching on GPUs. In contrast to previous approaches on the CPU which are based on depth-first traversal, GSM is BFS-based: possible matches are explored simultaneously in a breadth-first strategy. The advantage of using BFSbased traversal is that we can leverage the massively parallel processing capabilities of the GPU. The disadvantage is the generation of more intermediate results. We propose several optimization techniques to cope with the problem. Our implementation follows a filtering-and-verification strategy. While most previous work on GPUs requires one-/two-step joining, we use one-step verification to decide the candidates in current frontier of nodes. Our implementation has a speedup up to 4× over previous GPU state-of-the-art implementation.

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Section: Gpu-based Subgraph Matchingmentioning

confidence: 83%

“…Our algorithm is an extension of the triangle counting work from Wang et al [17] which follows a filtering-and-verification strategy. The filtering process prunes out candidates which cannot contribute to final matches based on certain constrains such as degree, label, and connections.…”

Section: Approachmentioning

confidence: 99%

Fast Gunrock Subgraph Matching (GSM) on GPUs

Wang,

Owens

2020

Preprint

Self Cite

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“…Triangle Counting. Many works perform triangle counting on the CPU [2,29,36,46] or the GPU [4,5,25,26,31,44,47,49,62,65]. A triangle is a 3-clique which is a special case of a 𝑘-clique.…”

Section: Related Workmentioning

confidence: 99%

Parallel K-clique counting on GPUs

Al-Masri

Hajj

Nagi

et al. 2022

Proceedings of the 36th ACM International Conference on Supercomputing

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Counting 𝑘-cliques in a graph is an important problem in graph analysis with many applications such as community detection and graph partitioning. Counting 𝑘-cliques is typically done by traversing search trees starting at each vertex in the graph. Parallelizing 𝑘-clique counting has been well-studied on CPUs and many solutions exist. However, there are no performant solutions for 𝑘-clique counting on GPUs.Parallelizing 𝑘-clique counting on GPUs comes with numerous challenges such as the need for extracting fine-grain multi-level parallelism, sensitivity to load imbalance, and constrained physical memory capacity. While there has been work on related problems such as finding maximal cliques and generalized sub-graph matching on GPUs, 𝑘-clique counting in particular has yet to be explored in depth. In this paper, we present the first parallel GPU solution specialized for the 𝑘-clique counting problem. Our solution supports both graph orientation and pivoting for eliminating redundant clique discovery. It incorporates both vertex-centric and edge-centric parallelization schemes for distributing work across thread blocks, and further partitions work within each thread block to extract fine-grain multi-level parallelism while tolerating load imbalance. It also includes optimizations such as binary encoding of induced sub-graphs and sub-warp partitioning to limit memory consumption and improve the utilization of execution resources.Our evaluation shows that our best GPU implementation outperforms the best state-of-the-art parallel CPU implementation by a geometric mean of 12.39×, 6.21×, and 18.99× for 𝑘 = 4, 7, and 10, respectively. We also perform a detailed evaluation of the trade-offs involved in the choice of parallelization scheme, and the incremental speedup of each optimization to provide an in-depth understanding of the optimization space. The insights from our optimization flow can be useful for optimizing other clique finding and graph mining solutions on GPUs. Our code will be open-sourced to enable further research on GPU parallelization of 𝑘-clique counting and other similar graph mining algorithms.

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“…From the earlier parallel node-iterator [51] family of algorithms, in recent years, a number of solutions have been developed targeting multi-core CPUs [63], GPUs [59], (heterogeneous) distributed platforms [42], as well as FPGAs [31]. Here, we focus on related work that targets distributed platforms.…”

Section: Relation To External Workmentioning

confidence: 99%

TriPoll: Computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata

Steil,

Reza,

Iwabuchi

et al. 2021

Preprint

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Understanding the higher-order interactions within network data is a key objective of network science. Surveys of metadata triangles (or patterned 3-cycles in metadata-enriched graphs) are often of interest in this pursuit. In this work, we develop TriPoll, a prototype distributed HPC system capable of surveying triangles in massive graphs containing metadata on their edges and vertices. We contrast our approach with much of the prior effort on triangle analysis, which often focuses on simple triangle counting, usually in simple graphs with no metadata. We assess the scalability of TriPoll when surveying triangles involving metadata on real and synthetic graphs with up to hundreds of billions of edges. We utilize communication-reducing optimizations to demonstrate a triangle counting task on a 224 billion edge web graph in approximately half of the time of competing approaches, while additionally supporting metadata-aware capabilities.

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Fast BFS-Based Triangle Counting on GPUs

Cited by 8 publications

References 9 publications

Fast Gunrock Subgraph Matching (GSM) on GPUs

Fast Gunrock Subgraph Matching (GSM) on GPUs

Parallel K-clique counting on GPUs

TriPoll: Computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata

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