FlowWalker: A Memory-efficient and High-performance GPU-based Dynamic Graph Random Walk Framework
Summary: FlowWalker: a GPU DGRW framework that removes GPU-global auxiliary buffers with an efficient parallel sampler to cut space complexity. Sampler-centric design plus dynamic scheduling mitigates power-law imbalance, yielding up to 752x/72x/16x speedups. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Junyi Mei
- 2. Shixuan Sun
- 3. Chao Li
- 4. Cheng Xu
- 5. Cheng Chen
- 6. Yibo Liu
- 7. Jing Wang
- 8. Cheng Zhao
- 9. Xiaofeng Hou
- 10. Minyi Guo
- 11. Bingsheng He
- 12. Xiaoliang Cong
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 278 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00029230623 |
| 1,329 | AGL: A Scalable System for Industrial-purpose Graph Machine Learning | 2020 | VLDB | 0.00012561848 |
| 5,177 | Remember Where You Came From: On The Second-Order Random Walk Based Proximity Measures | 2017 | VLDB | 5.6439245e-05 |
| 6,058 | ThunderRW: An In-Memory Graph Random Walk Engine | 2021 | VLDB | 5.2310254e-05 |
| 6,498 | Memory-Aware Framework for Efficient Second-Order Random Walk on Large Graphs | 2020 | SIGMOD | 5.0392468e-05 |
| 7,212 | Space-Efficient Random Walks on Streaming Graphs | 2023 | VLDB | 4.7989929e-05 |
| 8,993 | LightRW: FPGA Accelerated Graph Dynamic Random Walks | 2023 | SIGMOD | 4.4130611e-05 |
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