The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Multilevel hypergraph partitioning: applications in VLSI domain
Abstract
In this paper, we present a new hypergraph-partitioning algorithm that is based on the multilevel paradigm. In the multilevel paradigm, a sequence of successively coarser hypergraphs is constructed. A bisection of the smallest hypergraph is computed and it is used to obtain a bisection of the original hypergraph by successively projecting and refining the bisection to the next level finer hypergraph. We have developed new hypergraph coarsening strategies within the multilevel framework. We evaluate their performance both in terms of the size of the hyperedge cut on the bisection, as well as on the run time for a number of very large scale integration circuits. Our experiments show that our multilevel hypergraph-partitioning algorithm produces high-quality partitioning in a relatively small amount of time. The quality of the partitionings produced by our scheme are on the average 6%-23% better than those produced by other state-of-the-art schemes. Furthermore, our partitioning algorithm is significantly faster, often requiring 4-10 times less time than that required by the other schemes. Our multilevel hypergraph-partitioning algorithm scales very well for large hypergraphs. Hypergraphs with over 100 000 vertices can be bisected in a few minutes on today's workstations. Also, on the large hypergraphs, our scheme outperforms other schemes (in hyperedge cut) quite consistently with larger margins (9%-30%).
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Cites
(1979) · Cited by 43,841
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
Karypis, G., & Kumar, V. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827595287997 · Cited by 5,762
Self-citationAn Efficient Heuristic Procedure for Partitioning Graphs
Kernighan, B. W., & Lin, S. (1970) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1970.tb01770.x · Cited by 5,305
Combinatorial optimization: networks and matroids
Lawler, E. L. (2021) · Cited by 3,359
(1973) · North-Holland Mathematical Library · doi:10.1016/s0924-6509(09)x7013-3 · Cited by 2,937
A Linear-Time Heuristic for Improving Network Partitions
Fiduccia, C. M., & Mattheyses, R. M. (1982) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac.1982.1585498 · Cited by 2,567
(2003) · Selected Papers of Alan J Hoffman · doi:10.1142/9789812796936_others05 · Cited by 2,465
Partitioning Sparse Matrices with Eigenvectors of Graphs
Pothen, A., Simon, H. D., & Liou, K.-P. (1990) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/0611030 · Cited by 1,684
A multilevel algorithm for partitioning graphs
Hendrickson, B., & Leland, R. (1995) · Proceedings of the 1995 ACM/IEEE conference on Supercomputing (CDROM) - Supercomputing '95 · doi:10.1145/224170.224228 · Cited by 1,069
Welsh, D. J. A. (1974) · Bulletin of the London Mathematical Society · doi:10.1112/blms/6.2.218b · Cited by 1,046
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Sagi, O., & Rokach, L. (2018) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1249 · Cited by 3,103
Networks beyond pairwise interactions: Structure and dynamics
Battiston, F., Cencetti, G., Iacopini, I., Latora, V., Lucas, M., Patania, A., Young, J.-G., & Petri, G. (2020) · Physics Reports · doi:10.1016/j.physrep.2020.05.004 · Cited by 1,602
Comprehensive survey on hierarchical clustering algorithms and the recent developments
Ran, X., Xi, Y., Lu, Y., Wang, X., & Lu, Z. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10366-3 · Cited by 361
Metadata
- Authors
- Karypis, G., Aggarwal, R., Kumar, V., & Shekhar, S.
- Venue
- IEEE Transactions on Very Large Scale Integration (VLSI) Systems
- Published
- 1999-03
- Volume
- 7
- Issue
- 1
- Pages
- 69-79
- ISSN
- 1063-8210 (ISSN-L)
- 1557-9999
- OpenAlex
- W2078174680
- Cited by
- 800
- References
- 14
- FWCI
- 16.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- Karypis, G., Aggarwal, R., Kumar, V., & Shekhar, S. (1999). Multilevel hypergraph partitioning: applications in VLSI domain. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. https://doi.org/10.1109/92.748202
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