Optimized Parallel Breadth-First Search with Adaptive Strategies

Breadth-First Search (BFS) is a fundamental graph traversal algorithm in a level-by-level pattern. It has been widely used in real-world applications, such as social network analysis, scientific computing, and web crawling. However, achieving high performance for BFS on large-scale graphs remains a challenging task due to irregular memory access patterns, diverse graph structures, and the necessity for efficient parallelization. This paper addresses these challenges by designing a highly optimized parallel BFS implementation based on the top-down and bottom-up traversal strategies. It further integrates several key innovations, including graph type-aware computation strategy selection, graph pruning, two-level bottom-up, and efficient parallel implementation. We evaluate our method on 11 diverse graphs in terms of size, diameter, and density. On a CPU server with 48 threads, our method achieves an average speedup of 9.5× over the serial BFS implementation. Also, on a synthetic dense graph, our method processes 9.3 billion edges per second, showing its efficiency in dense graph traversal.