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Improving the Cache-Efficiency of Shortest Path Search

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 10505)

Abstract

Flood-filling algorithms as used for coloring images and shadow casting show that improved locality greatly increases the cache performance and, in turn, reduces the running time of an algorithm. In this paper we look at Dijkstra’s method to compute the shortest paths for example to generate pattern databases. As cache-improving contributions, we propose edge-cost factorization and flood-filling the memory layout of the graph. We conduct experiments in commercial game maps and compare the new priority queues with advanced heap implementations as well as and with alternative bucket implementations.

Keywords

  • Shortest Path Search
  • General Priority Queues
  • Flood Fill Algorithm
  • Pairing Heap
  • Radix Heap

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Stefan Edelkamp .

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Edelkamp, S. (2017). Improving the Cache-Efficiency of Shortest Path Search. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-67190-1_8

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