Graph Partitioning for Scalable Distributed Graph Computations
Inter-node communication time constitutes a significant fraction of the execution time of graph algorithms on distributed-memory systems. Global computations on large-scale sparse graphs with skewed degree distributions are particularly challenging to optimize for, as prior work shows that it is difficult to obtain balanced partitions with low edge cuts for these graphs. In this paper, the authors attempt to determine the optimal partitioning and distribution of such graphs, for load-balanced parallel execution of communication-intensive graph algorithms. They use Breadth-First Search (BFS) as a representative example, and derive upper bounds on the communication costs incurred with a two-dimensional partitioning of the graph.