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26
Multilevel hypergraph partitioning: Application in VLSI domain
 IEEE TRANS. VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
, 1999
"... In this paper, we present a new hypergraphpartitioning 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 origina ..."
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Cited by 315 (22 self)
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In this paper, we present a new hypergraphpartitioning 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 hypergraphpartitioning algorithm produces highquality 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 stateoftheart schemes. Furthermore, our partitioning algorithm is significantly faster, often requiring 4–10 times less time than that required by the other schemes. Our multilevel hypergraphpartitioning 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%).
Spectral Partitioning: The More Eigenvectors, the Better
 PROC. ACM/IEEE DESIGN AUTOMATION CONF
, 1995
"... The graph partitioning problem is to divide the vertices of a graph into disjoint clusters to minimize the total cost of the edges cut by the clusters. A spectral partitioning heuristic uses the graph's eigenvectors to construct a geometric representation of the graph (e.g., linear orderings) w ..."
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Cited by 75 (3 self)
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The graph partitioning problem is to divide the vertices of a graph into disjoint clusters to minimize the total cost of the edges cut by the clusters. A spectral partitioning heuristic uses the graph's eigenvectors to construct a geometric representation of the graph (e.g., linear orderings) which are subsequently partitioned. Our main result shows that when all the eigenvectors are used, graph partitioning reduces to a new vector partitioning problem. This result implies that as many eigenvectors as are practically possible should be used to construct a solution. This philosophy isincontrast to that of the widelyused spectral bipartitioning (SB) heuristic (which uses a single eigenvector to construct a 2way partitioning) and several previous multiway partitioning heuristics [7][10][16][26][37] (which usek eigenvectors to construct a kway partitioning). Our result motivates a simple ordering heuristic that is a multipleeigenvector extension of SB. This heuristic not only signi cantly outperforms SB, but can also yield excellent multiway VLSI circuit partitionings as compared to [1] [10]. Our experiments suggest that the vector partitioning perspective opens the door to new and effective heuristics.
HypergraphPartitioning Based Decomposition for Parallel SparseMatrix Vector Multiplication
 IEEE Trans. on Parallel and Distributed Computing
"... In this work, we show that the standard graphpartitioning based decomposition of sparse matrices does not reflect the actual communication volume requirement for parallel matrixvector multiplication. We propose two computational hypergraph models which avoid this crucial deficiency of the graph mo ..."
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Cited by 70 (34 self)
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In this work, we show that the standard graphpartitioning based decomposition of sparse matrices does not reflect the actual communication volume requirement for parallel matrixvector multiplication. We propose two computational hypergraph models which avoid this crucial deficiency of the graph model. The proposed models reduce the decomposition problem to the wellknown hypergraph partitioning problem. The recently proposed successful multilevel framework is exploited to develop a multilevel hypergraph partitioning tool PaToH for the experimental verification of our proposed hypergraph models. Experimental results on a wide range of realistic sparse test matrices confirm the validity of the proposed hypergraph models. In the decomposition of the test matrices, the hypergraph models using PaToH and hMeTiS result in up to 63% less communication volume (30%38% less on the average) than the graph model using MeTiS, while PaToH is only 1.32.3 times slower than MeTiS on the average. ...
Optimal Partitioners and Endcase Placers for Standardcell Layout
 IEEE TRANS. ON CAD
, 2000
"... We study alternatives to classic FMbased partitioning algorithms in the context of endcase processing for topdown standardcell placement. While the divide step in the topdown divide and conquer is usually performed heuristically, we observe that optimal solutions can be found for many su cientl ..."
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Cited by 62 (22 self)
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We study alternatives to classic FMbased partitioning algorithms in the context of endcase processing for topdown standardcell placement. While the divide step in the topdown divide and conquer is usually performed heuristically, we observe that optimal solutions can be found for many su ciently small partitioning instances. Our main motivation is that small partitioning instances frequently contain multiple cells that are larger than the prescribed partitioning tolerance, and that cannot be moved iteratively while preserving the legality ofa solution. To sample the suboptimality of FMbased partitioning algorithms, we focus on optimal partitioning and placement algorithms based on either enumeration or branchandbound that are invoked for instances below prescribed size thresholds,
Beyond pairwise clustering
 in IEEE Computer Society Conference on Computer Vision and Pattern Recognition
"... We consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the hypergraph partitioning problem. We propose a twostep algorithm for solving this problem. In the first step we use a nove ..."
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Cited by 61 (3 self)
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We consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the hypergraph partitioning problem. We propose a twostep algorithm for solving this problem. In the first step we use a novel scheme to approximate the hypergraph using a weighted graph. In the second step a spectral partitioning algorithm is used to partition the vertices of this graph. The algorithm is capable of handling hyperedges of all orders including order two, thus incorporating information of all orders simultaneously. We present a theoretical analysis that relates our algorithm to an existing hypergraph partitioning algorithm and explain the reasons for its superior performance. We report the performance of our algorithm on a variety of computer vision problems and compare it to several existing hypergraph partitioning algorithms. 1.
Spectral partitioning with multiple eigenvectors
 DISCRETE APPLIED MATHEMATICS
, 1999
"... The graph partitioning problem is to divide the vertices of a graph into disjoint clusters to minimize the total cost of the edges cut by the clusters. A spectral partitioning heuristic uses the graph’s eigenvectors to construct a geometric representation of the graph (e.g., linear orderings) which ..."
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Cited by 37 (0 self)
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The graph partitioning problem is to divide the vertices of a graph into disjoint clusters to minimize the total cost of the edges cut by the clusters. A spectral partitioning heuristic uses the graph’s eigenvectors to construct a geometric representation of the graph (e.g., linear orderings) which are subsequently partitioned. Our main result shows that when all the eigenvectors are used, graph partitioning reduces to a new vector partitioning problem. This result implies that as many eigenvectors as are practically possible should be used to construct a solution. This philosophy is in contrast to that of the widely used spectral hipartitioning (SB) heuristic (which uses only a single eigenvector) and several previous multiway partitioning heuristics [S, 11, 17, 27, 381 (which use k eigenvectors to construct kway partitionings). Our result motivates a simple ordering heuristic that is a multipleeigenvector extension of SB. This heuristic not only significantly outperforms recursive SB, but can also yield excellent multiway VLSI circuit partitionings as compared to [l, 111. Our experiments suggest that the vector partitioning perspective opens the door to new and effective partitioning heuristics. The present paper updates and improves a preliminary version of this work [5].
Efficient Network Flow Based MinCut Balanced Partitioning
, 1996
"... We consider the problem of bipartitioning a circuit into two balanced components that minimizes the number of crossing nets. Previously, Kernighan and Lin type (K&L) heuristics, simulated annealing approach, and analytical methods were given to solve the problem. However, network flow (maxflow ..."
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Cited by 17 (0 self)
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We consider the problem of bipartitioning a circuit into two balanced components that minimizes the number of crossing nets. Previously, Kernighan and Lin type (K&L) heuristics, simulated annealing approach, and analytical methods were given to solve the problem. However, network flow (maxflow mincut) techniques were overlooked as viable heuristics to mincut balanced bipartition due to their high complexity. In this paper we propose a balanced bipartition heuristic based on repeated maxflow mincut techniques, and give an efficient implementation that has the same asymptotic time complexity as that of one maxflow computation. We implemented our heuristic algorithm in a package called FBB. The experimental results demonstrate that FBB outperforms K&L heuristics and analytical methods in terms of the number of crossing nets, and our efficient implementation makes it possible to partition large circuit netlists with reasonable runtime. For example, the average elapsed time for bipartitioning a circuit S35932 of almost 20 K gates is less than 20 min on a SPARC10 with 32 MB memory.
Linear decomposition algorithm for VLSI design applications
 IN: PROC. IEEE INT. CONF. COMPUTERAIDED DESIGN
, 1995
"... We propose a unified solution to both linear placement and partitioning. Our approach combines the wellknown eigenvector optimization method with the recursive maxflow mincut method. A linearized eigenvector method is proposed to improve the linear placement. A hypergraph max
flow algorithm is th ..."
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Cited by 15 (1 self)
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We propose a unified solution to both linear placement and partitioning. Our approach combines the wellknown eigenvector optimization method with the recursive maxflow mincut method. A linearized eigenvector method is proposed to improve the linear placement. A hypergraph max
flow algorithm is then adopted to efficiently find the maxflow mincut. In our unied approach, the maxflow mincut provides an optimal ordered partition subject to the given seeds and the eigenvector placement provides heuristic information for seed selection. Experimental results on MCNC benchmarks show that our approach is superior to other methods for both linear placement and partitioning problems. On average, our approach yields an improvement of 45.1 % over eigenvector approach in terms of total wire length, and yields an improvement of 26.9 % over PARABOLI[6] in terms of cut size.