Improved external memory BFS implementations

Breadth first search (BPS) traversal on massive graphs in external memory was considered non-viable until recently, because of the large number of I/Os it incurs. Ajwani et al. [3] showed that the randomized variant of the o(n) I/O algorithm of Mehlhorn and Meyer [24] (MM.BFS) can compute the BPS level decomposition for large graphs (around a billion edges) in a few hours for small diameter graphs and a few days for large diameter graphs. We improve upon their implementation of this algorithm by reducing the overhead associated with each BPS level, thereby improving the results for large diameter graphs which are more difficult for BPS traversal in external memory. Also, we present the implementation of the deterministic variant of MM.BFS and show that in most cases, it outperforms the randomized variant. The running time for BPS traversal is further improved with a heuristic that preserves the worst case guarantees of MM_BFS, Together, they reduce the time for BFS on large diameter graphs from days shown in [3] to hours. In particular, on line graphs with random layout on disks, our implementation of the deterministic variant of MM_BFS with the proposed heuristic is more than 75 times faster than the previous best result for the randomized variant of MM.BFS in [3].