Geld: Load-balanced D-Core Decomposition for Consumer GPUs
Summary: Geld targets D-core decomposition on memory-constrained consumer GPUs with dynamic degree-aware load balancing and enumeration-based compact storage. It reduces contention and redundant work, achieving 31× over GPU peeling and 3 orders of magnitude over the best serial method. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Cheng Huang (Aarhus University)
- 2. Johannes Langguth (Simula Research Laboratory)
- 3. Xing Cai (University of Oslo)
- 4. Davide Mottin (Aarhus University)
- 5. Ira Assent (Aarhus University)
BibTeX Citation
@inproceedings{huang_sigmod26,
title = {{Geld: Load-balanced D-Core Decomposition for Consumer GPUs}},
author = {Huang, Cheng and Langguth, Johannes and Cai, Xing and Mottin, Davide and Assent, Ira},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802064},
url = {https://dl.acm.org/doi/10.1145/3802064},
year = {2026}
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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 |
|---|---|---|---|---|
| 3,102 | Efficient GPU-Accelerated Subgraph Matching | 2023 | SIGMOD | 7.7568687e-05 |
| 3,855 | Distributed D-core Decomposition over Large Directed Graphs | 2022 | VLDB | 7.0712144e-05 |
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