Parallel multilevel algorithms for hypergraph partitioning

Parallel multilevel algorithms for hypergraph partitioning

In this paper, we present parallel multilevel algorithms for the hypergraph partitioning problem. In particular, we describe schemes for parallel coarsening, parallel greedy k-way refinement and parallel multi-phase refinement. Using an asymptotic theoretical performance model, we derive the isoefficiency function for our algorithms and hence show that they are technically scalable when the max...

Parallel algorithms for hypergraph partitioning

Near-optimal decomposition is central to the efficient solution of numerous problems in scientific computing and computer-aided design. In particular, intelligent a priori partitioning of input data can greatly improve the runtime and scalability of large-scale parallel computations. Discrete data structures such as graphs and hypergraphs are used to formalise such partitioning problems, with h...

Multilevel Algorithms for Multi-Constraint Hypergraph Partitioning

Traditional hypergraph partitioning algorithms compute a bisection a graph such that the number of hyperedges that are cut by the partitioning is minimized and each partition has an equal number of vertices. The task of minimizing the cut can be considered as the objective and the requirement that the partitions will be of the same size can be considered as the constraint. In this paper we exte...

Multilevel Hypergraph Partitioning

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 experimen...