GraphLab: A Distributed Framework for Machine Learning in the Cloud

TL;DR

GraphLab is a new distributed framework that enables efficient parallel machine learning algorithms on cloud systems, significantly improving performance over traditional frameworks like Hadoop.

Contribution

The paper introduces the GraphLab abstraction for parallel ML, providing a formal model and an efficient distributed implementation for large-scale data processing.

Findings

GraphLab achieves orders of magnitude faster performance than Hadoop.

GraphLab performs comparably or better than hand-tuned MPI implementations.

Evaluation conducted on large-scale data with a 64-node EC2 cluster.

Abstract

Machine Learning (ML) techniques are indispensable in a wide range of fields. Unfortunately, the exponential increase of dataset sizes are rapidly extending the runtime of sequential algorithms and threatening to slow future progress in ML. With the promise of affordable large-scale parallel computing, Cloud systems offer a viable platform to resolve the computational challenges in ML. However, designing and implementing efficient, provably correct distributed ML algorithms is often prohibitively challenging. To enable ML researchers to easily and efficiently use parallel systems, we introduced the GraphLab abstraction which is designed to represent the computational patterns in ML algorithms while permitting efficient parallel and distributed implementations. In this paper we provide a formal description of the GraphLab parallel abstraction and present an efficient distributed implementation. We conduct a comprehensive evaluation of GraphLab on three state-of-the-art ML algorithms using real large-scale data and a 64 node EC2 cluster of 512 processors. We find that GraphLab achieves orders of magnitude performance gains over Hadoop while performing comparably or superior to hand-tuned MPI implementations.