Scalable hypergraph partitioning

Abstract

The interest in graph partitioning has become quite huge due to growing problem sizes. Therefore more abstract solutions are desirable. In this thesis, hypergraph partitioning is investigated since hypergraphs provide a better level of abstraction than normal graphs. Further, restreaming approaches are examined because the partitioning results of real time strategies are often not satisfiable. It will be shown that they can perform up to 15\% better than real time approaches and can sometimes even hold up to polynomial approaches. By putting more thought into the restreaming, the partitioning results become even better. This is shown empirical when proposing Fractional Restreaming a novel "Partial Forgetting" strategy. Meanwhile, the additional runtime needed is negligible compared to polynomial strategies. Finally SHP, a novel graph partitioning and evaluation framework is introduced.