2018/01/12 by Saad Mohamad, Mohamad, Saad, Abdelhamid Bouchachia +3 · 1 citation
Computer Science · Mathematics · #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Parallel #and Cluster Computing (cs.DC) #cs.DC #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1801.04289
7 pages, 8 figures, 1 table, 2 algorithms, The paper has been submitted for publication
arxiv created 2018/01/12 · arxiv updated 2018/01/16
Stochastic variational inference (SVI) employs stochastic optimization to scale up Bayesian computation to massive data. Since SVI is at its core a stochastic gradient-based algorithm, horizontal parallelism can be harnessed to allow larger scale inference. We propose a lock-free parallel implementation for SVI which allows distributed computations over multiple slaves in an asynchronous style. We show that our implementation leads to linear speed-up while guaranteeing an asymptotic ergodic convergence rate O(1/√(T) ) given that the number of slaves is bounded by √(T) (T is the total number of iterations). The implementation is done in a high-performance computing (HPC) environment using message passing interface (MPI) for python (MPI4py). The extensive empirical evaluation shows that our parallel SVI is lossless, performing comparably well to its counterpart serial SVI with linear speed-up.