Incidence Constraints in Hypergraph Partitioning on GPU

2026-04-15

AI Summary

This work addresses the NP-hard problem of hypergraph partitioning under constraints on partition size and incident-edge uniqueness by proposing a GPU-accelerated multilevel partitioning algorithm. The approach explicitly memoizes hypergraph incidence structures, exploits set sparsity to optimize nested iterations and set operations, and integrates constraint-aware design principles to substantially enhance computational efficiency. Experimental results demonstrate that the proposed method achieves up to a 940× speedup over state-of-the-art serial multilevel partitioners while simultaneously improving connectivity metrics by 2% to 26%.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Hypergraph partitioning is a pervasive NP-hard problem, and accelerating its computation on GPU can both slice time-to-solution and raise quality of results. In this work, we implement a multi-level hypergraph partitioning algorithm on GPU targeting a specific set of problem constraints: bounded per-partition size and distinct inbound hyperedges. Manipulating hypergraphs requires long orders of nested iterations, and enforcing these constraints introduces further set operations amidst them. Hence, we design algorithms around our problem's specifics, materializing the hypergraph's incidence structure in memory and exploiting set sparsity. Our results show competitive speedups as high as 940x and 2-26% better results in connectivity over a sequential multi-level partitioner.

Problem

• hypergraph partitioning
• incidence constraints
• GPU acceleration
• bounded partition size
• distinct inbound hyperedges

Innovation

• hypergraph partitioning
• GPU acceleration
• incidence constraints
• set sparsity
• multi-level algorithm