DGS: A GPU-based Adaptive Graph Sampling Framework

Graph sampling plays a critical role in graph learning applications, notably within Graph Neural Networks (GNNs). Typically, the performance of GPU-based graph sampling is determined by the efficiency of sampling kernels. Different sampling methods excel under different conditions, and no single method consistently outperforms others in all scenarios. As sampling applications become increasingly complex, graph-related sparse operations can dominate the computational workload, with performance heavily influenced by storage formats. In this paper, we propose DGS, a GPU-based graph sampling framework that can detach the kernel implementation from computation logic. In addition to sampling kernels, DGS jointly optimizes sparse graph kernels. It can adaptively switch between different execution strategies based on various inputs. Experiments show that DGS outperforms current state-of-the-art GPU sampling frameworks, achieving speedups ranging from 1.1 × to 92.0 ×. This adaptability and performance improvement establish DGS as a highly effective and efficient solution for diverse graph sampling scenarios.