Distributed Evolutionary Graph Partitioning

TL;DR

This paper introduces KaFFPaE, a distributed evolutionary algorithm that leverages a multilevel graph partitioner to efficiently improve partitioning results, outperforming previous methods on standard benchmarks.

Contribution

The paper presents a novel distributed evolutionary algorithm, KaFFPaE, utilizing new crossover and mutation operators based on KaFFPa for improved graph partitioning.

Findings

Improves or recomputes 76% of benchmark entries.

Achieves high-quality partitions quickly.

Outperforms existing methods on standard benchmarks.

Abstract

We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the Graph Partitioning Problem, which makes use of KaFFPa (Karlsruhe Fast Flow Partitioner). The use of our multilevel graph partitioner KaFFPa provides new effective crossover and mutation operators. By combining these with a scalable communication protocol we obtain a system that is able to improve the best known partitioning results for many inputs in a very short amount of time. For example, in Walshaw's well known benchmark tables we are able to improve or recompute 76% of entries for the tables with 1%, 3% and 5% imbalance.