research-article

HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering

Authors:

Zhuo FengAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 14, Pages 1 - 9

https://doi.org/10.1145/3508352.3549438

Published: 22 December 2022 Publication History

Abstract

This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances. Motivated by the latest theoretical framework for low-resistance-diameter decomposition of simple graphs, HyperEF aims at decomposing large hypergraphs into multiple node clusters with only a few inter-cluster hyperedges. The key component in HyperEF is a nearly-linear time algorithm for estimating hyperedge effective resistances, which allows incorporating the latest diffusion-based non-linear quadratic operators defined on hypergraphs. To achieve good runtime scalability, HyperEF searches within the Krylov subspace (or approximate eigensubspace) for identifying the nearly-optimal vectors for approximating the hyperedge effective resistances. In addition, a node weight propagation scheme for multilevel spectral hypergraph decomposition has been introduced for achieving even greater node coarsening ratios. When compared with state-of-the-art hypergraph partitioning (clustering) methods, extensive experiment results on real-world VLSI designs show that HyperEF can more effectively coarsen (decompose) hypergraphs without losing key structural (spectral) properties of the original hypergraphs, while achieving over 70× runtime speedups over hMetis and 20× speedups over HyperSF.

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Cited By

Anticev JAghdaei ACheng WFeng ZDe V(2024)SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural NetworksProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3656521(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3656521
Aghdaei AFeng ZDe V(2024)inGRASS: Incremental Graph Spectral Sparsification via Low-Resistance-Diameter DecompositionProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3656520(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3656520
Chen BLiu ZZhang YYu WKim T(2024)Boosting Graph Spectral Sparsification via Parallel Sparse Approximate Inverse of Cholesky FactorProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473809(866-871)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/ASP-DAC58780.2024.10473809

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HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering

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cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

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Published: 22 December 2022

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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design

October 30 - November 3, 2022

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Cited By

Anticev JAghdaei ACheng WFeng ZDe V(2024)SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural NetworksProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3656521(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3656521
Aghdaei AFeng ZDe V(2024)inGRASS: Incremental Graph Spectral Sparsification via Low-Resistance-Diameter DecompositionProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3656520(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3656520
Chen BLiu ZZhang YYu WKim T(2024)Boosting Graph Spectral Sparsification via Parallel Sparse Approximate Inverse of Cholesky FactorProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473809(866-871)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/ASP-DAC58780.2024.10473809

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