MAAMAW '92 Selected papers from the 4th European Workshop on on Modelling Autonomous Agents in a Multi-Agent World, Artificial Social Systems
A Framework for Three-Dimensional Simulation of Morphogenesis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Environment as a first class abstraction in multiagent systems
Autonomous Agents and Multi-Agent Systems
From Genes to Organisms Via the Cell: A Problem-Solving Environment for Multicellular Development
Computing in Science and Engineering
Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
A high performance agent based modelling framework on graphics card hardware with CUDA
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Cellular Level Agent Based Modelling on the Graphics Processing Unit
HIBI '09 Proceedings of the 2009 International Workshop on High Performance Computational Systems Biology
Efficient simulation of agent-based models on multi-GPU and multi-core clusters
Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
Reducing branch divergence in GPU programs
Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units
| Hi-index | 0.00 |
Nowadays the basic graphic cards contain more than 300 compute cores, which is expected to significantly increase the performances of parallel applications. In the past few years, several works were made concerning the simulation of Multi-agent Systems on the Graphics Processing Unit GPU. But they often use agents which have a position in the environment associated with an interaction radius. In this study we present a generic implementation on the GPU of reactive agents, which are formed of a set of positions. The communications between the agents are only indirect, and are possible by reading the trail left by other agents. In our system these trails diffuse in the environment. The problem posed is to compute the diffusion in an efficient way, otherwise the whole environment should be iterated. Our implementation takes advantage of the GPU by executing each agent and diffusion on one compute unit. Firstly, our implementation keeps the same dynamics in comparison to previous studies, where the agents were not executed in parallel. Secondly, the simulation time has a 45 × speed advantage over a 4 core CPU.