Parallel Flow-Based Hypergraph Partitioning

TL;DR

This paper introduces a shared-memory parallelization of flow-based hypergraph refinement, significantly accelerating the process for large hypergraphs while maintaining high partition quality.

Contribution

It presents a novel parallel scheduling scheme and a parallel maximum flow algorithm, integrated into a state-of-the-art hypergraph partitioner, enabling faster processing of large hypergraphs.

Findings

Achieves comparable partition quality to the best sequential methods.

Enables hypergraph partitioning on extremely large graphs with up to 1 billion pins.

Runs an order of magnitude faster with 10 threads.

Abstract

We present a shared-memory parallelization of flow-based refinement, which is considered the most powerful iterative improvement technique for hypergraph partitioning at the moment. Flow-based refinement works on bipartitions, so current sequential partitioners schedule it on different block pairs to improve $k$-way partitions. We investigate two different sources of parallelism: a parallel scheduling scheme and a parallel maximum flow algorithm based on the well-known push-relabel algorithm. In addition to thoroughly engineered implementations, we propose several optimizations that substantially accelerate the algorithm in practice, enabling the use on extremely large hypergraphs (up to 1 billion pins). We integrate our approach in the state-of-the-art parallel multilevel framework Mt-KaHyPar and conduct extensive experiments on a benchmark set of more than 500 real-world hypergraphs, to show that the partition quality of our code is on par with the highest quality sequential code (KaHyPar), while being an order of magnitude faster with 10 threads.