Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2024

Abstract

Subgraph counting is a fundamental component for many downstream applications such as graph representation learning and query optimization. Since obtaining the exact count is often intractable, there have been a plethora of approximation methods on graph sampling techniques. Nonetheless, the state-of-the-art sampling methods still require massive samples to produce accurate approximations on large data graphs. We propose gSWORD, a GPU framework that leverages the massive parallelism of GPUs to accelerate iterative sampling algorithms for subgraph counting. Despite the embarrassingly parallel nature of the samples, there are unique challenges in accelerating subgraph counting due to its irregular computation logic. To address these challenges, we introduce two GPU-centric optimizations: (1) sample inheritance, enabling threads to inherit samples from neighboring threads to avoid idling, and (2) warp streaming, effectively distributing workloads among threads through a streaming process. Moreover, we propose a CPU-GPU co-processing pipeline that overlaps the sampling and enumeration processes to mitigate the underestimation issue. Experimental results demonstrate that deploying state-of-the-art sampling algorithms on gSWORD can perform millions of samples per second. The co-processing pipeline substantially improves the estimation accuracy in the cases where existing methods encounter severe underestimations with negligible overhead.

Keywords

Graph sampling, Subgraph counting, GPU computing

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the ACM on Management of Data

Volume

2

Issue

12

First Page

15

Last Page

26

ISSN

2836-6573

Identifier

10.1145/3639288

Publisher

ACM

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Comments

SIGMOD International Conference on Management of Data 2024

Additional URL

https://doi.org/10.1145/3639288

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