Abstract

In this paper we develop parallel A* algorithms suitable for distributed-memory machines. In parallel A* algorithms, inefficiencies grow with the number of processors P used, causing performance to drop significantly at lower and intermediate work densities (the ratio of the problem size to P ). To alleviate this effect, we propose a novel parallel startup phase and efficient dynamic work distribution strategies, and thus improve the scalability of parallel A* search. We also tackle the problem of duplicate searching by different processors, by using work transfer as a means to partial duplicate pruning. The parallel startup scheme proposed requires only \Theta(logP ) time compared to \Theta(P ) time for sequential startup methods used in the past. Using the Traveling Salesman Problem (TSP) as our test case, we see that our work distribution strategies yield speedup improvements of more than 30% and 15% at lower and intermediate work densities, respectively, while requiring 20% to 45%...

Citations