Modeling Scalability of Distributed Machine Learning

TL;DR

This paper introduces a simple framework to estimate the scalability of distributed machine learning algorithms by modeling their time complexity and validating with experiments on deep learning and belief propagation.

Contribution

It proposes a novel framework for predicting scalability in distributed machine learning, validated through experiments and applied to Apache Spark.

Findings

Models accurately predict speedup with increasing nodes

Framework effectively estimates scalability for deep learning training

Applied successfully to Apache Spark environment

Abstract

Present day machine learning is computationally intensive and processes large amounts of data. It is implemented in a distributed fashion in order to address these scalability issues. The work is parallelized across a number of computing nodes. It is usually hard to estimate in advance how many nodes to use for a particular workload. We propose a simple framework for estimating the scalability of distributed machine learning algorithms. We measure the scalability by means of the speedup an algorithm achieves with more nodes. We propose time complexity models for gradient descent and graphical model inference. We validate our models with experiments on deep learning training and belief propagation. This framework was used to study the scalability of machine learning algorithms in Apache Spark.