Graph processing on GPUs is gaining momentum due to the high throughputs observed compared to traditional CPUs, attributed to the vast number of processing cores on GPUs that can exploit parallelism in graph analytics. This paper discusses a graph data structure for dynamic graph processing on GPUs. Unlike static graphs, dynamic graphs mutate over their lifetime through vertex and/or edge batch updates. The proposed work aims to provide fast batch updates and graph querying without consuming too much GPU memory. Experimental results show improved initialization timings by 1968-1269024%, improved batch edge insert timings by 30-30047%, and improved batch edge delete timings by 50-25262% while consuming less memory when the batch size is large.

翻译：GPU上的图处理正日益受到关注，这是因为与传统的CPU相比，GPU拥有大量处理核心，能够利用图分析中的并行性，从而实现更高的吞吐量。本文讨论了一种用于GPU上动态图处理的图数据结构。与静态图不同，动态图在其生命周期内会通过顶点和/或边的批量更新而发生突变。所提出的工作旨在在不消耗过多GPU内存的情况下，实现快速的批量更新和图查询。实验结果表明，当批量较大时，初始化时间改善了1968-1269024%，批量边插入时间改善了30-30047%，批量边删除时间改善了50-25262%，同时消耗的内存更少。