Hierarchical Parallelization and Runtime Scheduling for Pregel-like Graph Processing Systems

Graph processing has become popular for various big data analytic applications. Google's Pregel framework enables vertex-centric graph processing in distributed environment based on Bulk Synchronous Parallel (BSP) model. However, the BSP model is inefficient for many complex graph algorithms requiring graph traversals, as only a small number of vertices really update states in each superstep. In this paper, we propose an hierarchical parallelization mechanism, taking the advantages of both synchronous (warp-level) and asynchronous (task-level) parallelization approaches. In addition, a runtime task scheduling mechanism is proposed, relying on real-time monitoring or prediction of resource utilization. Experiments have verified that the hierarchical parallelization mechanism can expose greater parallelism, and thus, increase resource utilization significantly. Moreover, the runtime scheduling mechanism can avoid aggressive resource competition, and thus, further enhance the performance of the parallelized graph processing.