Study of a new parallel Monte Carlo neutron transport simulation model
Palavras-chave:
parallel programming, neutron transport.Resumo
In this work a hybrid parallel Monte Carlo based neutron transport simulation program has been
developed using Message-passing Interface (MPI) and Compute Unified Device Architecture (CUDA)
technologies. Such program is aimed to run on a GPU-Cluster, that means, a computer cluster in which
the nodes are provided with programmable Graphics Processing Units (GPU). A quite simple, but very
time consuming Monte Carlo simulation have been considered in order to shown that making use of an
uncomplicated and low cost computer architecture, it is possible to achieve great gains in terms of
computational performance. As an example, in the best case, a parallel simulation running on an 8-GPUcluster
(4 multi-core PC, with 2 GPU each) was more than 2000 times faster than the sequential program
running on a single processor. Here, the physical model, hardware and software architecture, as well as
results obtained in comparative experiments are described and comment.
Referências
Buck, I., 2007. GPU computing with nVIDIA CUDA. In: International
Conference on Computer Graphics and Interactive Techniques. ACM New
York, NY, USA.
He, Z., Harada, K., 2006. Solving point-feature labeling placement problem by parallel Hopfield neural network on GPU graphics card. Machine Graphics & Vision International Journal 15 (1), 99e120.
Heimlich, A., Mol, A.C.A., Pereira, C.M.N.A., 2011. GPU-based Monte Carlo simulation in neutron transport and finite differences heat equation evaluation. Progress in Nuclear Energy (New Series) 53, 229e239.
Luo, Z., Liu, H., Wu, X., 2005. Artificial neural network computation on graphic process unit. In: 2005 IEEE International Joint Conference on Neural Networks. Proceedings, vol. 1. IJCNN’05.
Pereira, C.M.N.A., Sacco, W.F., 2008. A parallel genetic algorithm with niching technique applied to a nuclear reactor core design optimization problem. Progress in Nuclear Energy (New Series) 50, 740e746.
Pereira, C.M.N.A., Lapa, C.M.F., 2003a. Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem. Annals of Nuclear Energy 30, 555e565.
Pereira, C.M.N.A., Lapa, C.M.F., 2003b. Parallel Island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization. Annals of Nuclear Energy 30, 1665e1675.
Tölke, J., 2008. Implementation of a lattice Boltzmann kernel using the compute unified device architecture developed by nVIDIA. Computing and Visualization in Science, 1e11.
Waintraub, M., Schirru, R., Pereira, C.M.N.A., 2009. Multiprocessor modeling of parallel particle swarm optimization applied to nuclear engineering problems. Progress in Nuclear Energy (New Series) 51, 680e688.
