The graph partitioning problem is widely used and studied in many practical
and theoretical applications. The multilevel strategies represent today one of
the most effective and efficient generic frameworks for solving this problem on
large-scale graphs. Most of the attention in designing the multilevel
partitioning frameworks has been on the refinement phase. In this work we focus
on the coarsening phase, which is responsible for creating structurally similar
to the original but smaller graphs.
We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the
Graph Partitioning Problem, which makes use of KaFFPa (Karlsruhe Fast Flow
Partitioner). The use of our multilevel graph partitioner KaFFPa provides new
effective crossover and mutation operators. By combining these with a scalable
communication protocol we obtain a system that is able to improve the best
known partitioning results for many inputs in a very short amount of time.
We describe an approach to parallel graph partitioning that scales to
hundreds of processors and produces a high solution quality. For example, for
many instances from Walshaw's benchmark collection we improve the best known
partitioning. We use the well known framework of multi-level graph
partitioning.