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Distributed computing excels at processing large scale data, but thecommunication cost for synchronizing the shared parameters may slow down theoverall performance. Fortunately, the interactions between parameter and datain many problems are sparse, which admits efficient partition in order toreduce the communication overhead.  In this paper, we formulate data placement as a graph partitioning problem.We propose a distributed partitioning algorithm. We give both theoreticalguarantees and a highly efficient implementation. We also provide a highlyefficient implementation of the algorithm and demonstrate its promising resultson both text datasets and social networks. We show that the proposed algorithmleads to 1.6x speedup of a state-of-the-start distributed machine learningsystem by eliminating 90\% of the network communication.

Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning