The Impossibility of Parallelizing Boosting

TL;DR

This paper proves that parallelizing boosting algorithms is fundamentally limited, requiring exponentially more resources, which challenges the feasibility of efficient parallel boosting methods.

Contribution

It provides a strong negative theoretical result showing that parallelizing boosting is inherently resource-intensive and likely impractical.

Findings

Parallel boosting cannot be significantly accelerated without exponential resource increase.

Theoretical proof of the inherent sequential nature of boosting algorithms.

Implication that boosting's sequential structure limits parallelization efforts.

Abstract

The aim of boosting is to convert a sequence of weak learners into a strong learner. At their heart, these methods are fully sequential. In this paper, we investigate the possibility of parallelizing boosting. Our main contribution is a strong negative result, implying that significant parallelization of boosting requires an exponential blow-up in the total computing resources needed for training.