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219
An Evaluation of Bipartitioning Techniques
 IEEE Transactions on ComputerAided Design
, 1997
"... Abstract — Logic partitioning is an important issue in VLSI CAD, and has been an area of active research for at least the last 25 years. Numerous approaches have been developed and many different techniques have been combined for a wide range of applications. In this paper, we examine many of the ex ..."
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Cited by 61 (6 self)
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Abstract — Logic partitioning is an important issue in VLSI CAD, and has been an area of active research for at least the last 25 years. Numerous approaches have been developed and many different techniques have been combined for a wide range of applications. In this paper, we examine many of the existing techniques for logic bipartitioning and present a methodology for determining the best mix of approaches. The result is a novel bipartitioning algorithm that includes both new and preexisting techniques. Our algorithm produces results that are at least 16% better than the state of the art while also being efficient in run time. I.
An Energy Model for Visual Graph Clustering
 Proceedings of the 11th International Symposium on Graph Drawing (GD 2003), LNCS 2912
, 2003
"... We introduce an energy model whose minimum energy drawings reveal the clusters of the drawn graph. Here a cluster is a set of nodes with many internal edges and few edges to nodes outside the set. The drawings of the bestknown force and energy models do not clearly show clusters for graphs whose ..."
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Cited by 59 (4 self)
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We introduce an energy model whose minimum energy drawings reveal the clusters of the drawn graph. Here a cluster is a set of nodes with many internal edges and few edges to nodes outside the set. The drawings of the bestknown force and energy models do not clearly show clusters for graphs whose diameter is small relative to the number of nodes. We formally characterize the minimum energy drawings of our energy model. This characterization shows in what sense the drawings separate clusters, and how the distance of separated clusters to the other nodes can be interpreted.
Improved Algorithms for Hypergraph Bipartitioning
 IN PROCEEDINGS OF THE ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE
, 2000
"... Multilevel FiducciaMattheyses (MLFM) hypergraph partitioning [3, 22, 24] is a fundamental optimization in VLSI CAD physical design. The leading implementation, hMetis [23], has since 1997 proved itself substantially superior in both runtime and solution quality to even very recent works (e.g., [13, ..."
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Cited by 59 (15 self)
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Multilevel FiducciaMattheyses (MLFM) hypergraph partitioning [3, 22, 24] is a fundamental optimization in VLSI CAD physical design. The leading implementation, hMetis [23], has since 1997 proved itself substantially superior in both runtime and solution quality to even very recent works (e.g., [13, 17, 25]). In this work, we present two sets of results: (i) new techniques for flat FMbased hypergraph partitioning (which is the core of multilevel implementations), and (ii) a new multilevel implementation that offers leadingedge performance. Our new techniques for flat partitioning confirm the conjecture from [10], suggesting that specialized partitioning heuristics may be able to actively exploit fixed nodes in partitioning instances arising in the driving topdown placement context. Our FM variant is competitive with traditional FM on instances without terminals [1] and considerably superior on instances with fixed nodes (i.e., arising during topdown placement [8]). Our multilevel ...
Permuting Sparse Rectangular Matrices into BlockDiagonal Form
 SIAM Journal on Scientific Computing
, 2002
"... We investigate the problem of permuting a sparse rectangular matrix into block diagonal form. Block diagonal form of a matrix grants an inherent parallelism for solving the deriving problem, as recently investigated in the context of mathematical programming, LU factorization and QR factorization. W ..."
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Cited by 56 (18 self)
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We investigate the problem of permuting a sparse rectangular matrix into block diagonal form. Block diagonal form of a matrix grants an inherent parallelism for solving the deriving problem, as recently investigated in the context of mathematical programming, LU factorization and QR factorization. We propose bipartite graph and hypergraph models to represent the nonzero structure of a matrix, which reduce the permutation problem to those of graph partitioning by vertex separator and hypergraph partitioning, respectively. Our experiments on a wide range of matrices, using stateoftheart graph and hypergraph partitioning tools MeTiS and PaToH, revealed that the proposed methods yield very effective solutions both in terms of solution quality and runtime.
Clustering software artifacts based on frequent common changes
 In Proceedings of the 13th IEEE International Workshop on Program Comprehension (IWPC 2005
, 2005
"... Changes of software systems are less expensive and less errorprone if they affect only one subsystem. Thus, clusters of artifacts that are frequently changed together are subsystem candidates. We introduce a twostep method for identifying such clusters. First, a model of common changes of softwa ..."
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Cited by 54 (9 self)
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Changes of software systems are less expensive and less errorprone if they affect only one subsystem. Thus, clusters of artifacts that are frequently changed together are subsystem candidates. We introduce a twostep method for identifying such clusters. First, a model of common changes of software artifacts, called cochange graph, is extracted from the version control repository of the software system. Second, a layout of the cochange graph is computed that reveals clusters of frequently cochanged artifacts. We derive requirements for such layouts, and introduce an energy model for producing layouts that fulfill these requirements. We evaluate the method by applying it to three example systems, and comparing the resulting layouts to authoritative decompositions. 1
Nature's way of optimizing
, 2000
"... We propose a generalpurpose method for finding highquality solutions to hard optimization problems, inspired by selforganizing processes often found in nature. The method, called Extremal Optimization, successively eliminates extremely undesirable components of suboptimal solutions. Drawing upon ..."
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Cited by 51 (6 self)
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We propose a generalpurpose method for finding highquality solutions to hard optimization problems, inspired by selforganizing processes often found in nature. The method, called Extremal Optimization, successively eliminates extremely undesirable components of suboptimal solutions. Drawing upon models used to simulate farfromequilibrium dynamics, it complements approximation methods inspired by equilibrium statistical physics, such as Simulated Annealing. With only one adjustable parameter, its performance proves competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.
Graph Partitioning Algorithms With Applications To Scientific Computing
 Parallel Numerical Algorithms
, 1997
"... Identifying the parallelism in a problem by partitioning its data and tasks among the processors of a parallel computer is a fundamental issue in parallel computing. This problem can be modeled as a graph partitioning problem in which the vertices of a graph are divided into a specified number of su ..."
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Cited by 50 (0 self)
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Identifying the parallelism in a problem by partitioning its data and tasks among the processors of a parallel computer is a fundamental issue in parallel computing. This problem can be modeled as a graph partitioning problem in which the vertices of a graph are divided into a specified number of subsets such that few edges join two vertices in different subsets. Several new graph partitioning algorithms have been developed in the past few years, and we survey some of this activity. We describe the terminology associated with graph partitioning, the complexity of computing good separators, and graphs that have good separators. We then discuss early algorithms for graph partitioning, followed by three new algorithms based on geometric, algebraic, and multilevel ideas. The algebraic algorithm relies on an eigenvector of a Laplacian matrix associated with the graph to compute the partition. The algebraic algorithm is justified by formulating graph partitioning as a quadratic assignment p...
A hybrid multilevel/genetic approach for circuit partitioning
 IN PROC. ACM/SIGDA PHYSICAL DESIGN WORKSHOP
, 1996
"... We present a genetic circuit partitioning algorithm that integrates the Metis graph partitioning package [15] originally designed for sparse matrix computations. Metis is an extremely fast iterative partitioner that uses multilevel clustering. We have adapted Metis to partition circuit netlists, and ..."
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Cited by 45 (7 self)
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We present a genetic circuit partitioning algorithm that integrates the Metis graph partitioning package [15] originally designed for sparse matrix computations. Metis is an extremely fast iterative partitioner that uses multilevel clustering. We have adapted Metis to partition circuit netlists, and have applied a genetic technique that uses previous Metis solutions to help construct new Metis solutions. Our hybrid technique produces better results than Metis alone, and also produces bipartitionings that are competitive with previous methods [20] [18] [6] while using less CPU time.
Edge Separability Based Circuit Clustering with Application to Circuit Partitioning
 IEEE/ACM Asia South Pacific Design Automation Conference
, 2000
"... In this paper, we introduce a new efficient O(n log n) graph search based bottomup clustering algorithm named ESC (Edge Separability based Clustering). Unlike existing bottomup algorithms that are based on local connectivity information of the netlist, ESC exploits more global connectivity inform ..."
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Cited by 45 (23 self)
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In this paper, we introduce a new efficient O(n log n) graph search based bottomup clustering algorithm named ESC (Edge Separability based Clustering). Unlike existing bottomup algorithms that are based on local connectivity information of the netlist, ESC exploits more global connectivity information "edge separability" to guide clustering process while carefully monitoring cluster area balance. Computing the edge separability for a given edge e = (x; y) in an edge weighted undirected graph G(V; E; s; w) is equivalent to finding the xy mincut. Then, we show that a simple and efficient algorithm CAPFOREST [14] can be used to provide a good estimation of edge separability for all edges in G without using any network flow computation. Related experiments based on large scale ISPD98 [1] benchmark circuits confirm that exploiting edge separability yields better quality partitioning solution compared to various bottomup clustering algorithms proposed in the literature including Absorpt...
ProbabilityBased Approaches to VLSI Circuit Partitioning
, 2000
"... Iterativeimprovement twoway mincut partitioning is an important phase in most circuit placement tools, and finds use in many other computeraided design (CAD) applications. Most iterative improvement techniques for circuit netlists like the Fiduccia–Mattheyses (FM) method compute the gains of no ..."
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Cited by 44 (6 self)
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Iterativeimprovement twoway mincut partitioning is an important phase in most circuit placement tools, and finds use in many other computeraided design (CAD) applications. Most iterative improvement techniques for circuit netlists like the Fiduccia–Mattheyses (FM) method compute the gains of nodes using local netlist information that is only concerned with the immediate improvement in the cutset. This can lead to misleading gain information. Krishnamurthy suggested a lookahead (LA) gain calculation method to ameliorate this situation; however, as we show, it leaves room for improvement. We present here a probabilistic gain computation approach called probabilistic partitioner (PROP) that is capable of capturing the future implications of moving a node at the current time. We also propose an extended algorithm SHRINKPROP that increases the probability of removing recently “perturbed ” nets (nets whose nodes have been moved for the first time) from the cutset. Experimental results on medium to largesize ACM/SIGDA benchmark circuits show that PROP and SHRINKPROP outperform previous iterativeimprovement methods like FM (by about 30 % and 37%, respectively) and LA (by about 27 % and 34%, respectively). Both PROP and SHRINKPROP also obtain much better cutsizes than many recent stateoftheart partitioners like EIG1, WINDOW, MELO, PARABOLI, GFM and GMetis (by 4.5 % to 67%). Our empirical timing results reveal that PROP is appreciably faster than most recent techniques. We also obtain results on the more recent ISPD98 benchmark suite that show similar substantial mincut improvements by PROP and SHRINKPROP over FM (24 % and 31%, respectively). It is also noteworthy that SHRINKPROP’s results are within 2.5 % of those obtained by hMetis, one of the best multilevel partitioners. However, the multilevel paradigm is orthogonal to SHRINKPROP. Further, since it is a “flat” partitioner, it has advantages over hMetis in partitiondriven placement applications.