  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.
