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Evaluation of Speedup of Monte Carlo Calculations of Two Simple Reactor Physics Problems Coded for the GPU/CUDA Environment

Aiping Ding, Tianyu Liu, Chao Liang, Wei Ji, Mark S. Shephard, X George Xu, Forrest B. Brown
Program of Nuclear Engineering and Engineering Physics, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2011), Rio de Janeiro, RJ, Brazil, May 8-12, 2011

@article{ding2011evaluation,

   title={EVALUATION OF SPEEDUP OF MONTE CARLO CALCULATIONS OF TWO SIMPLE REACTOR PHYSICS PROBLEMS CODED FOR THE GPU/CUDA ENVIRONMENT},

   author={Ding, A. and Liu, T. and Liang, C. and Ji, W. and Shephard, M.S. and Xu, X.G. and Brown, F.B.},

   year={2011}

}

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Monte Carlo simulation is ideally suited for solving Boltzmann neutron transport equation in inhomogeneous media. However, routine applications require the computation time to be reduced to hours and even minutes in a desktop system. The interest in adopting GPUs for Monte Carlo acceleration is rapidly mounting, fueled partially by the parallelism afforded by the latest GPU technologies and the challenge to perform full-size reactor core analysis on a routine basis. In this study, Monte Carlo codes for a fixed-source neutron transport problem and an eigenvalue/criticality problem were developed for CPU and GPU environments, respectively, to evaluate issues associated with computational speedup afforded by the use of GPUs. The results suggest that a speedup factor of 30 in Monte Carlo radiation transport of neutrons is within reach using the state-of-the-art GPU technologies. However, for the eigenvalue/criticality problem, the speedup was 8.5. In comparison, for a task of voxelizing unstructured mesh geometry that is more parallel in nature, the speedup of 45 was obtained. It was observed that, to date, most attempts to adopt GPUs for Monte Carlo acceleration were based on naive implementations and have not yielded the level of anticipated gains. Successful implementation of Monte Carlo schemes for GPUs will likely require the development of an entirely new code. Given the prediction that future-generation GPU products will likely bring exponentially improved computing power and performances, innovative hardware and software solutions may make it possible to achieve full-core Monte Carlo calculation within one hour using a desktop computer system in a few years.
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