  In this paper, we introduce a novel Distributed Markov Chain Monte Carlo
(MCMC) inference method for the Bayesian Non-Parametric Latent Block Model
(DisNPLBM), employing the Master/Worker architecture. Our non-parametric
co-clustering algorithm divides observations and features into partitions using
latent multivariate Gaussian block distributions. The workload on rows is
evenly distributed among workers, who exclusively communicate with the master
and not among themselves. DisNPLBM demonstrates its impact on cluster labeling
accuracy and execution times through experimental results. Moreover, we present
a real-use case applying our approach to co-cluster gene expression data. The
code source is publicly available at
https://github.com/redakhoufache/Distributed-NPLBM.
