MindTheStep-AsyncPSGD: Adaptive Asynchronous Parallel Stochastic Gradient Descent

TL;DR

This paper introduces MindTheStep-AsyncPSGD, an adaptive asynchronous SGD method that models staleness to improve convergence in high-dimensional non-convex optimization, with theoretical guarantees and empirical validation.

Contribution

It develops a new staleness model and an adaptive step size framework for AsyncPSGD, enhancing convergence analysis and practical performance in deep learning.

Findings

Faster convergence in deep learning tasks.

Provable reduction of asynchrony effects.

Effective online adaptation of step sizes.

Abstract

Stochastic Gradient Descent (SGD) is very useful in optimization problems with high-dimensional non-convex target functions, and hence constitutes an important component of several Machine Learning and Data Analytics methods. Recently there have been significant works on understanding the parallelism inherent to SGD, and its convergence properties. Asynchronous, parallel SGD (AsyncPSGD) has received particular attention, due to observed performance benefits. On the other hand, asynchrony implies inherent challenges in understanding the execution of the algorithm and its convergence, stemming from the fact that the contribution of a thread might be based on an old (stale) view of the state. In this work we aim to deepen the understanding of AsyncPSGD in order to increase the statistical efficiency in the presence of stale gradients. We propose new models for capturing the nature of the staleness distribution in a practical setting. Using the proposed models, we derive a staleness-adaptive SGD framework, MindTheStep-AsyncPSGD, for adapting the step size in an online-fashion, which provably reduces the negative impact of asynchrony. Moreover, we provide general convergence time bounds for a wide class of staleness-adaptive step size strategies for convex target functions. We also provide a detailed empirical study, showing how our approach implies faster convergence for deep learning applications.