FREIGHT: Fast Streaming Hypergraph Partitioning

Kamal Eyubov; Marcelo Fonseca Faraj; Christian Schulz

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arXiv · 2302.06259

FREIGHT: Fast Streaming Hypergraph Partitioning

Kamal Eyubov·Marcelo Fonseca Faraj·Christian Schulz

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Abstract

Partitioning the vertices of a (hyper)graph into k roughly balanced blocks such that few (hyper)edges run between blocks is a key problem for large-scale distributed processing. A current trend for partitioning huge (hyper)graphs using low computational resources are streaming algorithms. In this work, we propose FREIGHT: a Fast stREamInG Hypergraph parTitioning algorithm which is an adaptation of the widely-known graph-based algorithm Fennel. By using an efficient data structure, we make the overall running of FREIGHT linearly dependent on the pin-count of the hypergraph and the memory consumption linearly dependent on the numbers of nets and blocks. The results of our extensive experimentation showcase the promising performance of FREIGHT as a highly efficient and effective solution for streaming hypergraph partitioning. Our algorithm demonstrates competitive running time with the Hashing algorithm, with a difference of a maximum factor of four observed on three fourths of the instances. Significantly, our findings highlight the superiority of FREIGHT over all existing (buffered) streaming algorithms and even the in-memory algorithm HYPE, with respect to both cut-net and connectivity measures. This indicates that our proposed algorithm is a promising hypergraph partitioning tool to tackle the challenge posed by large-scale and dynamic data processing.

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Kamal Eyubov, Marcelo Fonseca Faraj, Christian Schulz. 2023-02-13. FREIGHT: Fast Streaming Hypergraph Partitioning. https://arxiv.org/abs/2302.06259

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