SGgraph: A Scalable GPU-Based Edge-Centric Graph Processing Framework

In today's interconnected world, data is often represented as graphs due to their ability to capture relationships between data entities. Recently, graph processing has gained significant interest in both academia and industry because it enables the extraction of valuable insights from these graphs. Due to their massive parallelism at a lower cost, GPUs have become the preferred choice for graph processing. However, fully exploiting the power of GPUs for graph processing is challenging due to the irregular nature of graphs, which is incompatible with GPU architecture. To address this challenge and facilitate the development of graph algorithms on GPUs, several graph processing frameworks have been developed. However, most current GPU graph processing frameworks struggle to handle real-world graphs because their size often exceeds the memory capacity of GPUs. Additionally, the preprocessing phase required by most frameworks often dominates the total execution time. In this paper, we propose SGgraph, a scalable GPU-based graph processing framework that makes graph computation compatible with GPU architecture without the need for preprocessing. Our framework can handle large graphs that do not fit in GPU memory and can process graphs with billions of edges on a single machine using multiple GPUs. Our experiments show that SGgraph outperforms existing GPU-based frameworks, achieving competitive processing time and remarkable reductions in total execution time, with improvements averaging a factor of 7.7 to 19.7.