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| Content Provider | ACM Digital Library |
|---|---|
| Author | Lages, Wallace Santos Silva, Alessandro Ribeiro Da Chaimowicz, Luiz |
| Abstract | Behavioral models have been used in the entertainment industry to increase the realism in the simulation of large groups of individuals. Unfortunately, the classical models can be very compute-intensive when very large groups are considered, reducing its applicability in games and other interactive systems. In this article we explore both search space reduction and parallelism to improve the performance of Reynold's Boids model. We propose a methodology that considers self-occlusion (visibility culling) to reduce the number of neighbors and we take advantage the parallelism present in common graphics processor units (GPUs) to allow the simulation of very large groups. We performed different GPU implementations (GPGPU and CUDA); the results show that visibility culling allows significant gains in performance without affecting the model's overall behavior. |
| Starting Page | 1 |
| Ending Page | 20 |
| Page Count | 20 |
| File Format | |
| ISSN | 15443574 |
| DOI | 10.1145/1658866.1658870 |
| Journal | Computers in Entertainment (CIE) (CIE) |
| Volume Number | 7 |
| Issue Number | 4 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2008-03-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Boids simulation Emergent behavior Gpgpu |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Applications |
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