A fast algorithm for particle simulations
Journal of Computational Physics
Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Astrophysical N-body simulations using hierarchical tree data structures
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Experiences with parallel N-body simulation
SPAA '94 Proceedings of the sixth annual ACM symposium on Parallel algorithms and architectures
Hierarchical Model for Real Time Simulation of Virtual Human Crowds
IEEE Transactions on Visualization and Computer Graphics
Better group behaviors in complex environments using global roadmaps
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
Proceedings of the 2006 ACM SIGGRAPH symposium on Videogames
A comparative study of partitioning methods for crowd simulations
Applied Soft Computing
The virtual marathon: parallel computing supports crowd simulations
IEEE Computer Graphics and Applications - Special issue on non-photorealistic rendering a virtual environment for teaching social skills
Parallelizing continuum crowds
Proceedings of the 17th ACM Symposium on Virtual Reality Software and Technology
Data-intensive document clustering on graphics processing unit (GPU) clusters
Journal of Parallel and Distributed Computing
Workload balancing in distributed crowd simulations: the partitioning method
The Journal of Supercomputing
A framework for distributing agent-based simulations
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing
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Group behaviors, e.g. birds flocking, are widely used in virtual reality, computer games, robotics and artificial life. While many methods to simulate group behaviors have been proposed, these methods are usually applied to sequential computing. Since most of these methods have a polynomial complexity, it is difficult to simulate a large group in real-time using these methods. In this paper, we propose a parallel algorithm to simulate the flocking behavior of a large group. The new partitioning and communication mechanisms in the parallel algorithm make the flocking simulation more efficient. Experimental results show that the proposed parallel algorithm provides good speedup in generating flocking behaviors compared with the sequential simulation.