Exploring the feasibility of heterogeneous computing of complex networks for big data analysis

We present our experience with exploring the configuration space for accelerating BFS's on large complex networks in the context of a heterogeneous GPU + CPU HPC platform. We study the feasibility of the heterogeneous computing approach by systematically studying different graph partitioning strategies while processing synthetic and real-world complex networks. To achieve this, we exploit the coreness of complex networks for load partitioning.