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.