Graph Analytics Through Fine-Grained Parallelism
Summary: Topology-aware, synchronous in-memory graph analytics with fine-grained parallelism. A hybrid 2PL/OCC scheme—2PL for high-degree, OCC for low-degree vertices—yields high throughput and correctness, outperforming BSP and asynchronous schedulers. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zechao Shang (Chinese University of Hong Kong)
- 2. Feifei Li (University of Utah)
- 3. Jeffrey Xu Yu (Chinese University of Hong Kong)
- 4. Zhiwei Zhang (Hong Kong Baptist University)
- 5. Hong Cheng (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{shang_sigmod16,
title = {{Graph Analytics Through Fine-Grained Parallelism}},
author = {Shang, Zechao and Li, Feifei and Yu, Jeffrey Xu and Zhang, Zhiwei and Cheng, Hong},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2915238},
url = {https://dl.acm.org/doi/10.1145/2882903.2915238},
year = {2016}
}
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