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Parallel Non-blocking Deterministic Algorithm for Online Topic Modeling

Part of the Communications in Computer and Information Science book series (CCIS,volume 661)


In this paper we present a new asynchronous algorithm for learning additively regularized topic models and discuss the main architectural details of our implementation. The key property of the new algorithm is that it behaves in a fully deterministic fashion, which is typically hard to achieve in a non-blocking parallel implementation. The algorithm had been recently implemented in the BigARTM library ( Our new algorithm is compatible with all features previously introduced in BigARTM library, including multimodality, regularizers and scores calculation. While the existing BigARTM implementation compares favorably with alternative packages such as Vowpal Wabbit or Gensim, the new algorithm brings further improvements in CPU utilization, memory usage, and spends even less time to achieve the same perplexity.


  • Probabilistic topic modeling
  • Additive regularization of topic models
  • Stochastic matrix factorization
  • EM-algorithm
  • Online learning
  • Asynchronous and parallel computing
  • BigARTM

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  • DOI: 10.1007/978-3-319-52920-2_13
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The work was supported by Russian Science Foundation (grant 15-18-00091). Also we would like to thank Prof. K. V. Vorontsov for constant attention to our research and detailed feedback to this paper.

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Correspondence to Oleksandr Frei or Murat Apishev .

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Frei, O., Apishev, M. (2017). Parallel Non-blocking Deterministic Algorithm for Online Topic Modeling. In: , et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham.

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