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Collaborative Diffusion on the GPU for Path-Finding in Games

Craig McMillan, Emma Hart, Kevin Chalmers
Edinburgh Napier University
17 March 2015

@incollection{mcmillan2015collaborative,

   title={Collaborative Diffusion on the GPU for Path-Finding in Games},

   author={McMillan, Craig and Hart, Emma and Chalmers, Kevin},

   booktitle={Applications of Evolutionary Computation},

   pages={418–429},

   year={2015},

   publisher={Springer}

}

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Exploiting the powerful processing power available on the GPU in many machines, we investigate the performance of parallelised versions of pathfinding algorithms in typical game environments. We describe a parallel implementation of a collaborative diffusion algorithm that is shown to find short paths in real-time across a range of graph sizes and provide a comparison to the well known Dijkstra and A* algorithms. Although some trade-off of cost vs path-length is observed under specific environmental conditions, results show that it is a viable contender for pathfinding in typical real-time game scenarios, freeing up CPU computation for other aspects of game AI.
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