| In this thesis, we identify several factors that are critical to high-performance GPU graph analytics: efficient and flexible building block operators, synchronization and data movement, workload distribution and load balancing, and memory access patterns. We analyze the impact of these critical factors through three high-level GPU graph analytic frameworks, VertexAPI2, MapGraph, and Gunrock. We also examine their effect on different workloads: four common graph primitives from multiple graph application domains, evaluated through real-world and synthetic graphs. We show that efficient building block operators enable more powerful operations for fast information propagation and result in fewer device kernel invocations, less data movement, and fewer global synchronizations, and thus are key focus areas for efficient large-scale graph analytics on GPUs. |
