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An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

Mark Joselli, Marcelo Zamith, Esteban Clua, Anselmo Montenegro, Regina Leal-Toledo, Aura Conci, Paulo Pagliosa, Luis Valente, Bruno Feijo
UFF – Media Lab, Medialab, Brazil
Computers in Entertainment (CIE) – SPECIAL ISSUE: Games, Volume 7, Issue 4, December 2009

@article{joselli2009adaptative,

   title={An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU},

   author={Joselli, M. and Zamith, M. and Clua, E. and Montenegro, A. and Leal-Toledo, R. and Conci, A. and Pagliosa, P. and Valente, L. and Feij{‘o}, B.},

   journal={Computers in Entertainment (CIE)},

   volume={7},

   number={4},

   pages={50},

   year={2009},

   publisher={ACM}

}

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This article presents a new architecture to implement all game loop models for games and real-time applications that use the GPU as a mathematics and physics coprocessor, working in parallel processing mode with the CPU. The presented model applies automatic task distribution concepts. The architecture can apply a set of heuristics defined in Lua scripts in order to get acquainted with the best processor for handling a given task. The model applies the GPGPU (general-purpose computation on GPUs) paradigm. In this article we propose an architecture that acquires knowledge about the hardware by running tasks in each processor and, by studying their performance over time, finding the best processor for a group of tasks.
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