DAWN: An Improved BFS Algorithm for Solving Shortest Path Problems on Unweighted Graph

The shortest paths problem is a fundamental challenge in graph theory, with a broad range of potential applications. However, traditional serial algorithms often struggle to adapt to large-scale graphs. To address this issue, researchers have explored parallel computing as a solution. We propose an improved BFS (Breadth-First Search) algorithm achieving higher parallelism and scalability, which requires $O(E_{wcc}(i))$ and $O(S_{wcc} \cdot E_{wcc})$ times for SSSP and APSP problems, respectively, where $S_{wcc}$ and $E_{wcc}$ denote the number of nodes and edges included in the largest weakly connected component in graph. To evaluate the effectiveness of the novel algorithm, we tested it using real graphs from Stanford Network Analysis Platform and SuiteSparse Matrix Collection. Our algorithm outperformed the BFS and $\Delta$-stepping implementations from Gunrock, achieving a speedup of 313.763$\times$ and 338.862$\times$, respectively.