2 citations · 3 across the 3 of their papers we have counts for
7 papers
Parallel Flow-Based Hypergraph Partitioning
Lars Gottesbüren, Tobias Heuer, Peter Sanders
We present a shared-memory parallelization of flow-based refinement, which is considered the most powerful iterative improvement technique for hypergraph partitioning at the moment…
High-Quality Hypergraph Partitioning
Sebastian Schlag, Tobias Heuer, Lars Gottesbüren +3
This paper considers the balanced hypergraph partitioning problem, which asks for partitioning the vertices into disjoint blocks of bounded size while minimizing an objective f…
Deep Multilevel Graph Partitioning
Lars Gottesbüren, Tobias Heuer, Peter Sanders +2
Partitioning a graph into blocks of "roughly equal" weight while cutting only few edges is a fundamental problem in computer science with a wide range of applications. In particula…
Shared-Memory n-level Hypergraph Partitioning
Lars Gottesbüren, Tobias Heuer, Peter Sanders +1
We present a shared-memory algorithm to compute high-quality solutions to the balanced -way hypergraph partitioning problem. This problem asks for a partition of the vertex set…
Multilevel Hypergraph Partitioning with Vertex Weights Revisited
Tobias Heuer, Nikolai Maas, Sebastian Schlag
The balanced hypergraph partitioning problem (HGP) is to partition the vertex set of a hypergraph into k disjoint blocks of bounded weight, while minimizing an objective function d…
Scalable Shared-Memory Hypergraph Partitioning
Lars Gottesbüren, Tobias Heuer, Peter Sanders +1
Hypergraph partitioning is an important preprocessing step for optimizing data placement and minimizing communication volumes in high-performance computing applications. To cope wi…