Memetic Multilevel Hypergraph Partitioning

Hypergraph partitioning has a wide range of important applications such as VLSI design or scientific computing. With focus on solution quality, we develop the first multilevel memetic algorithm to tackle the problem. Key components of our contribution are new effective multilevel recombination and mutation operations that provide a large amount of diversity. We perform a wide range of experiments on a benchmark set containing instances from application areas such VLSI, SAT solving, social networks, and scientific computing. Compared to the state-of-the-art hypergraph partitioning tools hMetis, PaToH, and KaHyPar, our new algorithm computes the best result on almost all instances

A Hypergraph Framework for Optimal Model-Based Decomposition of Design Problems

Decomposition of large engineering system models is desirable sinceincreased model size reduces reliability and speed of numericalsolution algorithms. The article presents a methodology for optimalmodel-based decomposition (OMBD) of design problems, whether or notinitially cast as optimization problems. The overall model isrepresented by a hypergraph and is optimally partitioned into weaklyconnected subgraphs that satisfy decomposition constraints. Spectralgraph-partitioning methods together with iterative improvementtechniques are proposed for hypergraph partitioning. A known spectralK-partitioning formulation, which accounts for partition sizes andedge weights, is extended to graphs with also vertex weights. TheOMBD formulation is robust enough to account for computationaldemands and resources and strength of interdependencies between thecomputational modules contained in the model.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44780/1/10589_2004_Article_136837.pd

Algorithms in computer-aided design of VLSI circuits.

With the increased complexity of Very Large Scale Integrated (VLSI) circuits,Computer Aided Design (CAD) plays an even more important role. Top-downdesign methodology and layout of VLSI are reviewed. Moreover, previouslypublished algorithms in CAD of VLSI design are outlined.In certain applications, Reed-Muller (RM) forms when implemented withAND/XOR or OR/XNOR logic have shown some attractive advantages overthe standard Boolean logic based on AND/OR logic. The RM forms implementedwith OR/XNOR logic, known as Dual Forms of Reed-Muller (DFRM),is the Dual form of traditional RM implemented with AND /XOR.Map folding and transformation techniques are presented for the conversionbetween standard Boolean and DFRM expansions of any polarity. Bidirectionalmulti-segment computer based conversion algorithms are also proposedfor large functions based on the concept of Boolean polarity for canonicalproduct-of-sums Boolean functions. Furthermore, another two tabular basedconversion algorithms, serial and parallel tabular techniques, are presented forthe conversion of large functions between standard Boolean and DFRM expansionsof any polarity. The algorithms were tested for examples of up to 25variables using the MCNC and IWLS'93 benchmarks.Any n-variable Boolean function can be expressed by a Fixed PolarityReed-Muller (FPRM) form. In order to have a compact Multi-level MPRM(MMPRM) expansion, a method called on-set table method is developed.The method derives MMPRM expansions directly from FPRM expansions.If searching all polarities of FPRM expansions, the MMPRM expansions withthe least number of literals can be obtained. As a result, it is possible to findthe best polarity expansion among 2n FPRM expansions instead of searching2n2n-1 MPRM expansions within reasonable time for large functions. Furthermore,it uses on-set coefficients only and hence reduces the usage of memorydramatically.Currently, XOR and XNOR gates can be implemented into Look-Up Tables(LUT) of Field Programmable Gate Arrays (FPGAs). However, FPGAplacement is categorised to be NP-complete. Efficient placement algorithmsare very important to CAD design tools. Two algorithms based on GeneticAlgorithm (GA) and GA with Simulated Annealing (SA) are presented for theplacement of symmetrical FPGA. Both of algorithms could achieve comparableresults to those obtained by Versatile Placement and Routing (VPR) toolsin terms of the number of routing channel tracks

High-Quality Hypergraph Partitioning

This dissertation focuses on computing high-quality solutions for the NP-hard balanced hypergraph partitioning problem: Given a hypergraph and an integer $k$, partition its vertex set into $k$ disjoint blocks of bounded size, while minimizing an objective function over the hyperedges. Here, we consider the two most commonly used objectives: the cut-net metric and the connectivity metric. Since the problem is computationally intractable, heuristics are used in practice - the most prominent being the three-phase multi-level paradigm: During coarsening, the hypergraph is successively contracted to obtain a hierarchy of smaller instances. After applying an initial partitioning algorithm to the smallest hypergraph, contraction is undone and, at each level, refinement algorithms try to improve the current solution. With this work, we give a brief overview of the field and present several algorithmic improvements to the multi-level paradigm. Instead of using a logarithmic number of levels like traditional algorithms, we present two coarsening algorithms that create a hierarchy of (nearly) $n$ levels, where $n$ is the number of vertices. This makes consecutive levels as similar as possible and provides many opportunities for refinement algorithms to improve the partition. This approach is made feasible in practice by tailoring all algorithms and data structures to the $n$-level paradigm, and developing lazy-evaluation techniques, caching mechanisms and early stopping criteria to speed up the partitioning process. Furthermore, we propose a sparsification algorithm based on locality-sensitive hashing that improves the running time for hypergraphs with large hyperedges, and show that incorporating global information about the community structure into the coarsening process improves quality. Moreover, we present a portfolio-based initial partitioning approach, and propose three refinement algorithms. Two are based on the Fiduccia-Mattheyses (FM) heuristic, but perform a highly localized search at each level. While one is designed for two-way partitioning, the other is the first FM-style algorithm that can be efficiently employed in the multi-level setting to directly improve $k$-way partitions. The third algorithm uses max-flow computations on pairs of blocks to refine $k$-way partitions. Finally, we present the first memetic multi-level hypergraph partitioning algorithm for an extensive exploration of the global solution space. All contributions are made available through our open-source framework KaHyPar. In a comprehensive experimental study, we compare KaHyPar with hMETIS, PaToH, Mondriaan, Zoltan-AlgD, and HYPE on a wide range of hypergraphs from several application areas. Our results indicate that KaHyPar, already without the memetic component, computes better solutions than all competing algorithms for both the cut-net and the connectivity metric, while being faster than Zoltan-AlgD and equally fast as hMETIS. Moreover, KaHyPar compares favorably with the current best graph partitioning system KaFFPa - both in terms of solution quality and running time