Scalable, fast, and lightweight system for large-scale topic modeling
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LightLDA is a distributed system for large scale topic modeling. It implements a distributed sampler that enables very large data sizes and models. LightLDA improves sampling throughput and convergence speed via a fast O(1) metropolis-Hastings algorithm, and allows small cluster to tackle very large data and model sizes through model scheduling and data parallelism architecture. LightLDA is implemented with C++ for performance consideration.

We have sucessfully trained big topic models (with trillions of parameters) on big data (Top 10% PageRank values of Bing indexed page, containing billions of documents) in Microsoft. For more technical details, please refer to our WWW'15 paper.

For documents, please view our website

Why LightLDA

The highlight features of LightLDA are

  • Scalable: LightLDA can train models with trillions of parameters on big data with billions of documents, a scale previous implementations cann't handle.
  • Fast: The sampler can sample millions of tokens per second per multi-core node.
  • Lightweight: Such big tasks can be trained with as few as tens of machines.

Quick Start

Run $ sh to build lightlda. Run $ sh example/ for a simple example.


Please cite LightLDA if it helps in your research:

  title={LightLDA: Big Topic Models on Modest Computer Clusters},
  author={Yuan, Jinhui and Gao, Fei and Ho, Qirong and Dai, Wei and Wei, Jinliang and Zheng, Xun and Xing, Eric Po and Liu, Tie-Yan and Ma, Wei-Ying},
  booktitle={Proceedings of the 24th International Conference on World Wide Web},
  organization={International World Wide Web Conferences Steering Committee}

Microsoft Open Source Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact with any additional questions or comments.