PimPam: Efficient Graph Pattern Matching on Real Processing-in-Memory Hardware
Summary: PimPam maps graph pattern matching onto real commercial PIM hardware, attacking the memory-wall bottleneck with bandwidth-rich near-data execution. Key ideas: load-aware tasking, parallel partitioning, adaptive threading, and dynamic bitmaps for set intersection; 22.5x avg speedup over CPU baselines. (summarized by gpt-5.4-mini on May 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shuangyu Cai (Tsinghua University)
- 2. Boyu Tian (Tsinghua University)
- 3. Huanchen Zhang (Shanghai Qi Zhi Institute; Tsinghua University)
- 4. Mingyu Gao (Shanghai Qi Zhi Institute; Tsinghua University)
BibTeX Citation
@inproceedings{cai_sigmod24,
title = {{PimPam: Efficient Graph Pattern Matching on Real Processing-in-Memory Hardware}},
author = {Cai, Shuangyu and Tian, Boyu and Zhang, Huanchen and Gao, Mingyu},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654964},
url = {https://dl.acm.org/doi/10.1145/3654964},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,368 | Subgraph Matching: A New Decomposition Based Approach | 2025 | VLDB | 5.1843659e-05 |
| 10,498 | Octopus: Efficient Hypergraph Pattern Mining with Practical Processing-in-Memory Architecture | 2026 | SIGMOD | 4.9769913e-05 |
| 10,823 | X-Wim: Massive Parallelization of Weighted Matching in Bipartite Graphs | 2026 | VLDB | 4.9769913e-05 |
| 11,320 | X-Blossom: Massive Parallelization of Graph Maximum Matching | 2025 | VLDB | 4.9769913e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012087459 |
| 713 | Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins | 2019 | VLDB | 0.00014571507 |
| 1,252 | Distributed Evaluation of Subgraph Queries Using Worst-case Optimal Low-Memory Dataflows | 2018 | VLDB | 0.00011334813 |
| 2,468 | GPU-Accelerated Subgraph Enumeration on Partitioned Graphs | 2020 | SIGMOD | 8.4138334e-05 |
| 2,469 | Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU | 2020 | VLDB | 8.412568e-05 |
| 2,519 | Fractal: A General-Purpose Graph Pattern Mining System | 2019 | SIGMOD | 8.3494241e-05 |
| 3,012 | Efficient GPU-Accelerated Subgraph Matching | 2023 | SIGMOD | 7.7512751e-05 |
| 6,828 | PIM-tree: A Skew-resistant Index for Processing-in-Memory | 2023 | VLDB | 5.6690684e-05 |
| 7,943 | GraphINC: Graph Pattern Mining at Network Speed | 2023 | SIGMOD | 5.4197795e-05 |
Previous
Page 1 / 1
Next