FREIGHT: Fast Streaming Hypergraph Partitioning

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 wo...

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Veröffentlicht in:Algorithmica Jg. 87; H. 3; S. 405 - 428
Hauptverfasser: Eyubov, Kamal, Fonseca Faraj, Marcelo, Schulz, Christian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.03.2025
Springer Nature B.V
Schlagworte:
Algorithm Analysis and Problem Complexity
Algorithms
Apexes
Computer Science
Computer Systems Organization and Communication Networks
Data processing
Data structures
Data Structures and Information Theory
Distributed processing
Graph theory
Graphs
Hash based algorithms
Mathematics of Computing
Partitioning
Run time (computers)
Theory of Computation
Graph partitioning
Streaming algorithms
Algorithm engineering
Hypergraph partitioning
ISSN:0178-4617, 1432-0541
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Zusammenfassung: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 geometric mean runtime within a factor of four compared to the Hashing algorithm. 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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ISSN:0178-4617
1432-0541
DOI:10.1007/s00453-024-01291-8