A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization

TL;DR

This paper presents a vertex programming approach using hypergraph partitioning for large-scale consensus optimization, significantly reducing communication costs and improving runtime performance in big data analysis tasks.

Contribution

It introduces a novel hypergraph partitioning technique that enhances vertex programming efficiency and demonstrates its effectiveness in large-scale consensus optimization tasks.

Findings

Achieved 50% runtime improvement over existing partitioning schemes.

Reduced node replication by up to an order of magnitude.

Demonstrated scalability on realistic and synthetic graph datasets.

Abstract

In modern data science problems, techniques for extracting value from big data require performing large-scale optimization over heterogenous, irregularly structured data. Much of this data is best represented as multi-relational graphs, making vertex programming abstractions such as those of Pregel and GraphLab ideal fits for modern large-scale data analysis. In this paper, we describe a vertex-programming implementation of a popular consensus optimization technique known as the alternating direction of multipliers (ADMM). ADMM consensus optimization allows elegant solution of complex objectives such as inference in rich probabilistic models. We also introduce a novel hypergraph partitioning technique that improves over state-of-the-art partitioning techniques for vertex programming and significantly reduces the communication cost by reducing the number of replicated nodes up to an order of magnitude. We implemented our algorithm in GraphLab and measure scaling performance on a variety of realistic bipartite graph distributions and a large synthetic voter-opinion analysis application. In our experiments, we are able to achieve a 50% improvement in runtime over the current state-of-the-art GraphLab partitioning scheme.