research-article

GPU-Accelerated Subgraph Enumeration on Partitioned Graphs

Authors:

Kian-Lee TanAuthors Info & Claims

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

Pages 1067 - 1082

https://doi.org/10.1145/3318464.3389699

Published: 31 May 2020 Publication History

Abstract

Subgraph enumeration is important for many applications such as network motif discovery and community detection. Recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration, but they can only handle graphs that fit into the GPU memory. In this paper, we propose a new approach for GPU-accelerated subgraph enumeration that can efficiently scale to large graphs beyond the GPU memory. Our approach divides the graph into partitions, each of which fits into the GPU memory. The GPU processes one partition at a time and searches the matched subgraphs of a given pattern (i.e., instances) within the partition as in the small graph. The key challenge is on enumerating the instances across different partitions, because this search would enumerate considerably redundant subgraphs and cause the expensive data transfer cost via the PCI-e bus. Therefore, we propose a novel shared execution approach to eliminate the redundant subgraph searches and correctly generate all the instances across different partitions. The experimental evaluation shows that our approach can scale to large graphs and achieve significantly better performance than the existing single-machine solutions.

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Cited By

Zhou QYin PYan XLi CJiang GCheng J(2024)Atom: An Efficient Query Serving System for Embedding-based Knowledge Graph Reasoning with Operator-level BatchingProceedings of the ACM on Management of Data10.1145/36771292:4(1-29)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677129
Yan DYuan LAhmad AZheng CChen HCheng JBaeza-Yates RBonchi F(2024)Systems for Scalable Graph Analytics and Machine Learning: Trends and MethodsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671472(6627-6632)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671472
Park SOh SKim MLee IChabbi MSteuwer M(2024)INFINEL: An efficient GPU-based processing method for unpredictable large output graph queriesProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3627535.3638490(147-159)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1145/3627535.3638490
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Index Terms

GPU-Accelerated Subgraph Enumeration on Partitioned Graphs
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Graphics processors
  2. Parallel computing methodologies
    1. Parallel algorithms
      1. Massively parallel algorithms
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms
      2. Graph enumeration

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cover image ACM Conferences

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

June 2020

2925 pages

ISBN:9781450367356

DOI:10.1145/3318464

General Chairs:
David Maier
Portland State University, USA
,
Rachel Pottinger
University of British Columbia, Canada
,
Program Chairs:
AnHai Doan
University of Wisconsin, USA
,
Wang-Chiew Tan
Megagon Labs, USA
,
Publications Chairs:
Abdussalam Alawini
University of Illinois at Urbana-Champaign, USA
,
Hung Q. Ngo
RelationalAI, USA

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Published: 31 May 2020

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Ministry of Education - Singapore

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SIGMOD/PODS '20: International Conference on Management of Data

June 14 - 19, 2020

OR, Portland, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Zhou QYin PYan XLi CJiang GCheng J(2024)Atom: An Efficient Query Serving System for Embedding-based Knowledge Graph Reasoning with Operator-level BatchingProceedings of the ACM on Management of Data10.1145/36771292:4(1-29)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677129
Yan DYuan LAhmad AZheng CChen HCheng JBaeza-Yates RBonchi F(2024)Systems for Scalable Graph Analytics and Machine Learning: Trends and MethodsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671472(6627-6632)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671472
Park SOh SKim MLee IChabbi MSteuwer M(2024)INFINEL: An efficient GPU-based processing method for unpredictable large output graph queriesProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3627535.3638490(147-159)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1145/3627535.3638490
Huang KCui YYe QZhao YZhao XTian YZheng KHu HZhou X(2024)TED$^+$: Towards Discovering Top-k Edge-Diversified Patterns in a Graph DatabaseIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3312566(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3312566
Wei YJiang P(2024)GCSM: GPU-Accelerated Continuous Subgraph Matching for Large Graphs2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS57955.2024.00097(1046-1057)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPS57955.2024.00097
Qiu LChen LJie HKe XGao YLiu YZhang Z(2024)GPU-Accelerated Batch-Dynamic Subgraph Matching2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00248(3204-3216)Online publication date: 13-May-2024
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Qiu LLi ZKe XChen LGao Y(2024)Accelerating Biclique Counting on GPU2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00247(3191-3203)Online publication date: 13-May-2024
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Yuan LAhmad AYan DHan JAdhikari SYu XZhou Y(2024) G 2 -AIMD: A Memory-Efficient Subgraph-Centric Framework for Efficient Subgraph Finding on GPUs 2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00245(3164-3177)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00245
Yuan LYan DHan JAhmad AZhou YJiang Z(2024)Faster Depth-First Subgraph Matching on GPUs2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00244(3151-3163)Online publication date: 13-May-2024
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Song SLee HKim YLim JChoi DBok KYoo J(2024)Graph Processing Scheme Using GPU With Value-Driven Differential SchedulingIEEE Access10.1109/ACCESS.2024.337451312(41590-41600)Online publication date: 2024
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