Degree-Aware Kernel Mapping for Graph Processing on GPUs.

Social network graphs follow a power-law distribution, enabling us to exploit the GPU's hierarchical execution model for efficient computation through a degree-aware kernel computation approach. In this approach, nodes are mapped to different levels of parallelism on the GPU, depending upon their in-degree. To take advantage of this execution model, the nodes must be arranged in decreasing order of in-degree. However, doing so distorts the community structure (a property responsible for providing memory locality during computation), impacting the degree-aware kernel's performance gain. To balance ordering by in-degree and community structure, we propose DRBS, a graph reordering algorithm that sorts the graph nodes while preserving enough community structure to enhance cache efficiency.