The paper gives an overview of our recent work on balanced graph partitioning - partition the nodes of a graph into k blocks such that all blocks have approximately equal size and such that the number of cut edges is small. This problem has numerous applications for example in parallel processing. We report on a scalable parallelization and a number of improvements on the classical multilevel approach which leads to improved partitioning quality. This includes an integration of flow methods, improved local search, several improved coarsening schemes, repeated runs similar to the approaches used in multigrid solvers, and an integration into a distributed evolutionary algorithm. Overall this leads to a system that for many common benchmarks leads to both the best quality solution known and favorable tradeoffs between running time and solution quality.
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