GPH: An Efficient and Effective Perfect Hashing Scheme for GPU Architectures
Summary: GPH is a GPU-based perfect-hashing hash table guaranteeing a single bucket probe per lookup, with a micro-benchmark and analytic model for uniform performance evaluation. It exploits vectorization and ILP for global-memory efficiency and adds an insert kernel for dynamic updates; achieves about 8500 MOPS on synthetic and real workloads. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiaping Cao (Hong Kong Polytechnic University; Southern University of Science and Technology)
- 2. Le Xu (Hong Kong Polytechnic University; Southern University of Science and Technology)
- 3. Man Lung Yiu (Hong Kong Polytechnic University)
- 4. Jianbin Qin (Shenzhen University)
- 5. Bo Tang (Southern University of Science and Technology)
BibTeX Citation
@inproceedings{cao_sigmod25,
title = {{GPH: An Efficient and Effective Perfect Hashing Scheme for GPU Architectures}},
author = {Cao, Jiaping and Xu, Le and Yiu, Man Lung and Qin, Jianbin and Tang, Bo},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725406},
url = {https://dl.acm.org/doi/10.1145/3725406},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,252 | GraphRTX: Lighting the Way to Scalable Graph Analytics | 2026 | SIGMOD | 5.093636e-05 |
| 10,409 | TQEx: Tensor-based Query Engine Enhanced by Bridging the Gap | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 252 | Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited | 2014 | VLDB | 0.00023242719 |
| 631 | Relational Joins on Graphics Processors | 2008 | SIGMOD | 0.00015591241 |
| 1,466 | A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics | 2020 | SIGMOD | 0.0001068941 |
| 2,498 | Mega-KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores | 2015 | VLDB | 8.5016595e-05 |
| 4,996 | TAOBench: An End-to-End Benchmark for Social Network Workloads | 2022 | VLDB | 6.4081254e-05 |
| 5,053 | RTIndex: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing | 2023 | VLDB | 6.3844953e-05 |
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