{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T07:05:47Z","timestamp":1765177547499,"version":"3.41.0"},"reference-count":25,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2009,12,1]],"date-time":"2009-12-01T00:00:00Z","timestamp":1259625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Comput. Entertain."],"published-print":{"date-parts":[[2009,12]]},"abstract":"<jats:p>\n            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\n            <jats:italic>Boids<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.1145\/1658866.1658870","type":"journal-article","created":{"date-parts":[[2010,1,5]],"date-time":"2010-01-05T15:05:08Z","timestamp":1262703908000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Boids that see"],"prefix":"10.1145","volume":"7","author":[{"given":"Alessandro Ribeiro Da","family":"Silva","sequence":"first","affiliation":[{"name":"Universidade Federal de Minas Gerais"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wallace Santos","family":"Lages","sequence":"additional","affiliation":[{"name":"Universidade Federal de Minas Gerais"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luiz","family":"Chaimowicz","sequence":"additional","affiliation":[{"name":"Universidade Federal de Minas Gerais"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2010,1]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Breitbart J. 2008. Case studies on GPU usage and data structure design. Master's thesis Universit\u00e4t Kassel.  Breitbart J. 2008. Case studies on GPU usage and data structure design. Master's thesis Universit\u00e4t Kassel."},{"key":"e_1_2_1_2_1","unstructured":"Chiara R. D. Erra U. Scarano V. and Tatafiore M. 2004. Massive simulation using GPU of a distributed behavioral model of a flock with obstacle avoidance. In Vision Modeling and Visualization (VMV) 233--240.  Chiara R. D. Erra U. Scarano V. and Tatafiore M. 2004. Massive simulation using GPU of a distributed behavioral model of a flock with obstacle avoidance. 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Woo M. Neider J. and Davis T. 2005. OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R) Vers. 2 (5th ed.) Addison-Wesley Reading MA.   Shreiner D. Woo M. Neider J. and Davis T. 2005. OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R) Vers. 2 (5th ed.) Addison-Wesley Reading MA."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/2945.466719"},{"key":"e_1_2_1_23_1","unstructured":"Thalmann D. and Musse S. 2007. Crowd Simulation Springer Berlin.   Thalmann D. and Musse S. 2007. 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