GPU Accelerated Pathfinding
| dc.contributor.author | Bleiweiss, Avi | en_US |
| dc.contributor.editor | David Luebke and John Owens | en_US |
| dc.date.accessioned | 2013-10-28T10:19:26Z | |
| dc.date.available | 2013-10-28T10:19:26Z | |
| dc.date.issued | 2008 | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.seriesinformation | Graphics Hardware | en_US |
| dc.identifier.isbn | 978-3-905674-09-5 | en_US |
| dc.identifier.issn | 1727-3471 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/EGGH/EGGH08/065-074 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Categories and Subject Descriptors (according to ACM CCS): [Artificial Intelligence] I.2.8 Problem Solving, Control Methods, and Search Graph and Tree Search Strategies I.3.1 | en_US |
| dc.title | GPU Accelerated Pathfinding | en_US |
Files
Original bundle
1 - 1 of 1