• A Distributed Framework for Latent Variable Models
NOMAD is the alias for Non-locking, stOchastic Multi-machine framework for Asynchronous and Decentralized computation. This is a scalable distributed framework for various latent variable models. We have successfully applied NOMAD to two applications:
  • Low-rank Matrix Factorization for Large-scale Recommender Systems
  • Collapsed Gibbs Sampling for Large-scale Topic Modeling


  • NOMAD for Low-rank Matrix Factorization [download]
  • NOMAD for Collapsed Gibbs Sampling for LDA [download]


This software is released under the GPLv3 License but please acknowledge its use with a citation to at least one of the following publications: