Parallel Bi-objective shortest paths using weight-balanced B-trees with bulk updates

Abstract

We present a practical parallel algorithm for finding shortest paths in the presence of two objective functions. The algorithm builds on a recent theoretical result that on the first glance looks impractical. We address the problem of significant constant factor overheads due to numerous prefix sum computations by carefully re-engineering the algorithm for moderate parallelism. In addition, we develop a parallel weight-balanced B-tree data structure that cache efficiently supports bulk updates. This result might be of independent interest and closes the gap between the full-blown search tree data structure required by the theoretical result over the simple priority queue for the sequential algorithm. Comparing our implementation against a highly tuned sequential bi-objective search, we achieve speedups of 8 on 16 cores. © 2014 Springer International Publishing.