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Towards High-Throughput Gibbs Sampling at Scale: A Study across Storage Managers

Summary: Investigates high-throughput Gibbs sampling for large factor graphs, showing materialization, page layout, and buffer choices must be redesigned for beyond-memory workloads. On HBase and Unix-file backends, a simple prototype achieves competitive throughput for graphs larger than memory, outperforming baselines by up to 100x. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4697
Venue
SIGMOD
Year
2013
Pagerank
6.9967272e-05
Overall Rank
3,952 | 72.89%
DOI
10.1145/2463676.2463702

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhang_sigmod13,
        title = {{Towards High-Throughput Gibbs Sampling at Scale: A Study across Storage Managers}},
        author = {Zhang, Ce and Ré, Christopher},
        series = {{SIGMOD} '13},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2463676.2463702},
        url = {https://dl.acm.org/doi/10.1145/2463676.2463702},
        year = {2013}
}

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