Article

Optimality, scalability and stability study of partitioning and placement algorithms

Authors:

Michail Romesis,

Min XieAuthors Info & Claims

ISPD '03: Proceedings of the 2003 international symposium on Physical design

Pages 88 - 94

https://doi.org/10.1145/640000.640021

Published: 06 April 2003 Publication History

Abstract

This paper studies the optimality, scalability and stability of state-of-the-art partitioning and placement algorithms. We present algorithms to construct two classes of benchmarks, one for partitioning and the other for placement, which have known upper bounds of their optimal solutions, and can match any given net distribution vector. Using these partitioning and placement benchmarks, we studied the optimality of state-of-the-art algorithms by comparing their solutions with the upper bounds of the optimal solutions, and their scalability and stability by varying the sizes and characteristics of the benchmarks. The conclusions from this study are: 1) State-of-the-art, multilevel two way partitioning algorithms scale very well and are able to find solutions very close to the upper bounds of the optimal solutions of our benchmarks. This suggests that existing circuit partitioning techniques are fairly mature. There is not much room for improvement for cutsize minimization for problems of the current sizes. Multiway partitioning algorithms, on the other hand, do not perform that well. Their results can be up to 18% worse than our estimated upper bounds. 2) The state-of-the-art placement algorithms produce significantly inferior results compared with the estimated optimal solutions. There is still significant room for improvement in circuit placement. 3) Existing placement algorithms are not stable. Their effectiveness varies considerably depending on the characteristics of the benchmarks. New hybrid techniques are probably needed for future generation placement engines that are more scalable and stable.

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Cited By

Schlag SHeuer TGottesbüren LAkhremtsev YSchulz CSanders P(2023)High-Quality Hypergraph PartitioningACM Journal of Experimental Algorithmics10.1145/352909027(1-39)Online publication date: 10-Feb-2023
https://dl.acm.org/doi/10.1145/3529090
Lebedev OPurchina OFugarov D(2023)Modified Adaptive Particle Swarm AlgorithmProceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23)10.1007/978-3-031-43789-2_18(200-209)Online publication date: 21-Sep-2023
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Lebedev BLebedev OZhiglaty A(2021)Binary Decision Tree Construction using the Hybrid Swarm IntelligenceHerald of the Bauman Moscow State Technical University. Series Instrument Engineering10.18698/0236-3933-2021-2-52-65(52-65)Online publication date: Jun-2021
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Index Terms

Optimality, scalability and stability study of partitioning and placement algorithms
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)

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Published In

cover image ACM Conferences

ISPD '03: Proceedings of the 2003 international symposium on Physical design

April 2003

218 pages

ISBN:1581136501

DOI:10.1145/640000

General Chair:
Massoud Pedram
USC
,
Program Chair:
Charles J. Alpert
IBM

Copyright © 2003 ACM.

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Published: 06 April 2003

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April 6 - 9, 2003

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Overall Acceptance Rate 62 of 172 submissions, 36%

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Cited By

Schlag SHeuer TGottesbüren LAkhremtsev YSchulz CSanders P(2023)High-Quality Hypergraph PartitioningACM Journal of Experimental Algorithmics10.1145/352909027(1-39)Online publication date: 10-Feb-2023
https://dl.acm.org/doi/10.1145/3529090
Lebedev OPurchina OFugarov D(2023)Modified Adaptive Particle Swarm AlgorithmProceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23)10.1007/978-3-031-43789-2_18(200-209)Online publication date: 21-Sep-2023
https://doi.org/10.1007/978-3-031-43789-2_18
Lebedev BLebedev OZhiglaty A(2021)Binary Decision Tree Construction using the Hybrid Swarm IntelligenceHerald of the Bauman Moscow State Technical University. Series Instrument Engineering10.18698/0236-3933-2021-2-52-65(52-65)Online publication date: Jun-2021
https://doi.org/10.18698/0236-3933-2021-2-52-65
Kahng ALienig JBehjat LYang S(2021)Advancing PlacementProceedings of the 2021 International Symposium on Physical Design10.1145/3439706.3446884(15-22)Online publication date: 22-Mar-2021
https://dl.acm.org/doi/10.1145/3439706.3446884
Lebedev BLebedev OLebedeva E(2021)Bioinspired Multi-memetic AlgorithmFuturistic Trends in Network and Communication Technologies10.1007/978-981-16-1480-4_35(391-401)Online publication date: 31-Mar-2021
https://doi.org/10.1007/978-981-16-1480-4_35
Oleg BArtemy AOlga A(2021)Co-evolutionary Approach to Solving the Problem of VLSI Partitioning Using the Ant Colony MethodArtificial Intelligence in Intelligent Systems10.1007/978-3-030-77445-5_32(347-357)Online publication date: 16-Jul-2021
https://doi.org/10.1007/978-3-030-77445-5_32
Kureichik VKuliev EKureichik V(2020)Integrated algorithm for elements placement on the printed circuit boardIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/734/1/012146734(012146)Online publication date: 29-Jan-2020
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Veselov GLebedev BLebedev O(2020)Management of Behavior of a Swarm of Robots Applicable to the Tasks of Monitoring a Some TerritoryArtificial Intelligence and Bioinspired Computational Methods10.1007/978-3-030-51971-1_26(324-332)Online publication date: 9-Aug-2020
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Lebedev BLebedev OZhiglaty A(2020)Coverage with Sets Based on the Integration of Swarm Intelligence and Genetic EvolutionProceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19)10.1007/978-3-030-50097-9_49(487-496)Online publication date: 23-Jun-2020
https://doi.org/10.1007/978-3-030-50097-9_49
Heuer TSanders PSchlag S(2019)Network Flow-Based Refinement for Multilevel Hypergraph PartitioningACM Journal of Experimental Algorithmics10.1145/332987224(1-36)Online publication date: 5-Sep-2019
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