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GPU accelerated pathfinding

Avi Bleiweiss
NVIDIA Corporation
In GH ’08: Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware (2008), pp. 65-74.

@conference{bleiweiss2008gpu,

   title={GPU accelerated pathfinding},

   author={Bleiweiss, A.},

   booktitle={Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware},

   pages={65–74},

   year={2008},

   organization={Eurographics Association}

}

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In the past few years the graphics programmable processor (GPU) has evolved into an increasingly convincing computational resource for non graphics applications. The GPU is especially well suited to address problem sets expressed as data parallel computation with the same program executed on many data elements concurrently. In pursuing a scalable navigation planning approach for many thousands of agents in crowded game scenes, developers became more attracted to decomposable movement algorithms that lend to explicit parallelism. Pathfinding is one key computational intelligence action in games that is typified by intense search over sparse graph data structures. This paper describes an efficient GPU implementation of parallel global pathfinding using the CUDA programming environment, and demonstrates GPU performance scale advantage in executing an inherently irregular and divergent algorithm.
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