  The Bulk Synchronous Parallel(BSP) computational model has emerged as the
dominant distributed framework to build large-scale iterative graph processing
systems. While its implementations(e.g., Pregel, Giraph, and Hama) achieve high
scalability, frequent synchronization and communication among the workers can
cause substantial parallel inefficiency. To help address this critical concern,
this paper introduces the GraphHP(Graph Hybrid Processing) platform which
inherits the friendly vertex-centric BSP programming interface and optimizes
its synchronization and communication overhead.
  To achieve the goal, we first propose a hybrid execution model which
differentiates between the computations within a graph partition and across the
partitions, and decouples the computations within a partition from distributed
synchronization and communication. By implementing the computations within a
partition by pseudo-superstep iteration in memory, the hybrid execution model
can effectively reduce synchronization and communication overhead while not
requiring heavy scheduling overhead or graph-centric sequential algorithms. We
then demonstrate how the hybrid execution model can be easily implemented
within the BSP abstraction to preserve its simple programming interface.
Finally, we evaluate our implementation of the GraphHP platform on classical
BSP applications and show that it performs significantly better than the
state-of-the-art BSP implementations. Our GraphHP implementation is based on
Hama, but can easily generalize to other BSP platforms.
