Quantifying the performance impact of graph structure on neighbour iteration strategies for PageRank

Increases in graph size and analytics complexity have brought graph processing at the forefront of HPC. However, the HPC shift towards manycore accelerators (e.g., GPUs) is not favourable: traditional graph processing is hardly suitable for regular parallelism. Previous work has focused on parallel algorithms for specific graph operations, often using assumptions about the structure of the input graph. However, there has been very little systematic investigation of how strongly a graph’s structure impacts the efficiency of graph operations. With this article we make propose a step to quantify this impact, focusing on a typical operation: neighbour iteration. We design and implement four strategies for neighbour iteration and introduce a simple model to reason about the expected impact of a graph’s structure on the performance of each strategy. We then use the PageRank algorithm to validate our model. We show that performance is significantly affected by the ability to effectively load-balance the work performed by these strategies across the GPU’s cores