research-article

Pangolin: an efficient and flexible graph mining system on CPU and GPU

Authors:

Roshan Dathathri,

Gurbinder Gill,

Keshav PingaliAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 13, Issue 8

Pages 1190 - 1205

https://doi.org/10.14778/3389133.3389137

Published: 01 April 2020 Publication History

Abstract

There is growing interest in graph pattern mining (GPM) problems such as motif counting. GPM systems have been developed to provide unified interfaces for programming algorithms for these problems and for running them on parallel systems. However, existing systems may take hours to mine even simple patterns in moderate-sized graphs, which significantly limits their real-world usability.

We present Pangolin, an efficient and flexible in-memory GPM framework targeting shared-memory CPUs and GPUs. Pangolin is the first GPM system that provides high-level abstractions for GPU processing. It provides a simple programming interface based on the extend-reduce-filter model, which allows users to specify application specific knowledge for search space pruning and isomorphism test elimination. We describe novel optimizations that exploit locality, reduce memory consumption, and mitigate the overheads of dynamic memory allocation and synchronization.

Evaluation on a 28-core CPU demonstrates that Pangolin outperforms existing GPM frameworks Arabesque, RStream, and Fractal by 49×, 88×, and 80× on average, respectively. Acceleration on a V100 GPU further improves performance of Pangolin by 15× on average. Compared to state-of-the-art hand-optimized GPM applications, Pangolin provides competitive performance with less programming effort.

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Cited By

Hu LLin YZou LÖzsu M(2025)A graph pattern mining framework for large graphs on GPUThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00883-834:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s00778-024-00883-8
Cai STian BZhang HGao M(2024)PimPam: Efficient Graph Pattern Matching on Real Processing-in-Memory HardwareProceedings of the ACM on Management of Data10.1145/36549642:3(1-25)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654964
Zhang ZLu YZheng WLin X(2024)A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and InteractionProceedings of the ACM on Management of Data10.1145/36393152:1(1-29)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639315
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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 13, Issue 8

April 2020

172 pages

ISSN:2150-8097

Editors:
Magdalena Balazinska
University of Washington
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Xiaofang Zhou
University of Queensland, Australia

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Published: 01 April 2020

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Cited By

Hu LLin YZou LÖzsu M(2025)A graph pattern mining framework for large graphs on GPUThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00883-834:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s00778-024-00883-8
Cai STian BZhang HGao M(2024)PimPam: Efficient Graph Pattern Matching on Real Processing-in-Memory HardwareProceedings of the ACM on Management of Data10.1145/36549642:3(1-25)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654964
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