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Partitioning a graph into balanced blocks such that few edges run betweenblocks is a key problem for large-scale distributed processing. A current trendfor partitioning huge graphs are streaming algorithms, which use lowcomputational resources. In this work, we present a shared-memory streamingmulti-recursive partitioning scheme that performs recursive multi-sections onthe fly without knowing the overall input graph. Our approach has aconsiderably lower running time complexity in comparison with state-of-the-artnon-buffered one-pass partitioning algorithms for the standard graphpartitioning case. Moreover, if the topology of a distributed system is known,it is possible to further optimize the communication costs by mappingpartitions onto processing elements. Our experiments indicate that ouralgorithm is both faster and produces better process mappings than competingtools. In case of graph partitioning, our framework is up to two orders ofmagnitude faster at the cost of 5% more cut edges compared to Fennel.

Recursive Multi-Section on the Fly: Shared-Memory Streaming Algorithms for Hierarchical Graph Partitioning and Process Mapping