Removing Sequential Bottleneck of Dijkstra's Algorithm for the Shortest Path Problem

TL;DR

This paper introduces four new algorithms that reduce sequential bottlenecks in Dijkstra's shortest path algorithm by maintaining bounds, enabling more parallelism and improved complexity in certain graph types.

Contribution

The paper presents four novel algorithms that generalize previous work, reducing sequential dependencies and increasing parallelism in shortest path computations.

Findings

Algorithms $SP_1$ and $SP_2$ have optimal $O(e)$ complexity for certain graph types.

$SP_3$ and $SP_4$ enable more parallelism with higher work complexity.

The methods generalize prior parallelization approaches for Dijkstra's algorithm.

Abstract

All traditional methods of computing shortest paths depend upon edge-relaxation where the cost of reaching a vertex from a source vertex is possibly decreased if that edge is used. We introduce a method which maintains lower bounds as well as upper bounds for reaching a vertex. This method enables one to find the optimal cost for multiple vertices in one iteration and thereby reduces the sequential bottleneck in Dijkstra's algorithm. We present four algorithms in this paper --- $SP_1$, $SP_2$, $SP_3$ and $SP_4$. $SP_1$ and $SP_2$ reduce the number of heap operations in Dijkstra's algorithm. For directed acyclic graphs, or directed unweighted graphs they have the optimal complexity of $O(e)$ where $e$ is the number of edges in the graph which is better than that of Dijkstra's algorithm. For general graphs, their worst case complexity matches that of Dijkstra's algorithm for a sequential implementation but allows for greater parallelism. Algorithms $SP_3$ and $SP_4$ allow for even more parallelism but with higher work complexity. Algorithm $SP_3$ requires $O(n + e(\max(\log n, \Delta)))$ work where $n$ is the number of vertices and $\Delta$ is the maximum in-degree of a node. Algorithm $SP_4$ has the most parallelism. It requires $O(ne)$ work. These algorithms generalize the work by Crauser, Mehlhorn, Meyer, and Sanders on parallelizing Dijkstra's algorithm.