Asynchronous Stochastic Variational Inference

TL;DR

This paper introduces a lock-free asynchronous parallel implementation of stochastic variational inference (SVI) that achieves linear speed-up in distributed computing environments while maintaining convergence guarantees.

Contribution

The authors develop a novel lock-free parallel SVI algorithm for distributed systems, enabling scalable Bayesian inference with proven convergence properties.

Findings

Achieves linear speed-up in distributed environments.

Maintains comparable performance to serial SVI.

Convergence rate of O(1/√T) under bounded slave count.

Abstract

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 $O(1/\sqrt(T)$ ) given that the number of slaves is bounded by $\sqrt(T)$ ($T$ 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.