WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation

Summary: WarpLDA is a cache-aware O(1) per-token LDA that analyzes per-document memory access to maximize L3 cache locality. Achieves 5–15× speedups over LightLDA with 11B tokens/s throughput, enabling a million topics on 639M documents in five hours. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 11551
Venue: VLDB
Year: 2016
Pagerank: 5.7303405e-05
Overall Rank: 6,969 | 52.19%
DOI: 10.14778/2977797.2977801

Incoming Non-self Citations Over Time

Authors

1. Jianfei Chen (Tsinghua University)
2. Kaiwei Li (Tsinghua University)
3. Jun Zhu (Tsinghua University)
4. Wenguang Chen (Tsinghua University)

BibTeX Citation

@article{chen_vldb16,
        title = {{WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation}},
        author = {Chen, Jianfei and Li, Kaiwei and Zhu, Jun and Chen, Wenguang},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {10},
        pages = {744--755},
        doi = {10.14778/2977797.2977801},
        url = {https://doi.org/10.14778/2977797.2977801},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,999	LDA*: A Robust and Large-scale Topic Modeling System	2017	VLDB	5.093636e-05
13,529	Scalable Training of Hierarchical Topic Models	2018	VLDB	-

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank

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