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Spatial Data Structures, Sorting and GPU Parallelism for Situated-agent Simulation and Visualisation

A.V. Husselmann, K.A. Hawick
Computer Science, Institute for Information and Mathematical Sciences, Massey University, North Shore 102-904, Auckland, New Zealand
CSTN Computational Science Technical Note Series, CSTN-156, 2012

@article{husselmann2012spatial,

   title={Spatial Data Structures, Sorting and GPU Parallelism for Situated-agent Simulation and Visualisation},

   author={Husselmann, A.V. and Hawick, K.A.},

   year={2012}

}

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Spatial data partitioning techniques are important for obtaining fast and efficient simulations of N-Body particle and spatial agent based models where they considerably reduce redundant entity interaction computation times. Highly parallel techniques based on concurrent threading can be deployed to further speed up such simulations. We study the use of GPU accelerators and highly data parallel techniques which require more complex organisation of spatial datastructures and also sorting techniques to make best use of GPU capabilities. We report on a multiple-GPU (mGPU) solution to grid-boxing for accelerating interaction-based models. Our system is able to both simulate and also graphically render in excess of 10^5-10^6 agents on desktop hardware in interactive-time.
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