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GPU accelerated simulations of 3D deterministic particle transport using discrete ordinates method

Chunye Gong, Jie Liu, Lihua Chi, Haowei Huang, Jingyue Fang, Zhenghu Gong
School of Computer Science, National University of Defense Technology, Changsha 410073, China
Journal of Computational Physics (April 2011)

@article{gong2011gpu,

   title={GPU accelerated simulations of 3D deterministic particle transport using discrete ordinates method},

   author={Gong, C. and Liu, J. and Chi, L. and Huang, H. and Fang, J. and Gong, Z.},

   journal={Journal of Computational Physics},

   issn={0021-9991},

   year={2011},

   publisher={Elsevier}

}

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Graphics Processing Unit (GPU), originally developed for real-time, high-definition 3D graphics in computer games, now provides great faculty in solving scientific applications. The basis of particle transport simulation is the time-dependent, multi-group, inhomogeneous Boltzmann transport equation. The numerical solution to the Boltzmann equation involves the discrete ordinates (Sn) method and the procedure of source iteration. In this paper, we present a GPU accelerated simulation of one energy group time-independent deterministic discrete ordinates particle transport in 3D Cartesian geometry (Sweep3D). The performance of the GPU simulations are reported with the simulations of vacuum boundary condition. The discussion of the relative advantages and disadvantages of the GPU implementation, the simulation on multi GPUs, the programming effort and code portability are also reported. The results show that the overall performance speedup of one NVIDIA Tesla M2050 GPU ranges from 2.56 compared with one Intel Xeon X5670 chip to 8.14 compared with one Intel Core Q6600 chip for no flux fixup. The simulation with flux fixup on one M2050 is 1.23 times faster than on one X5670.
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