A Feasible Parallel Monte Carlo Algorithm to Simulate Templated Grain Growth

Xun Luo; Wei Zhao; Bao Shun Liu; Yan Ming Zhang

doi:10.4028/www.scientific.net/AMR.332-334.1868

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 332-334A Feasible Parallel Monte Carlo Algorithm to...

A Feasible Parallel Monte Carlo Algorithm to Simulate Templated Grain Growth

Abstract:

It is proposed a parallel Monte Carlo algorithm to simulate templated grain growth in sintering ceramics materials. The algorithm applies the general Potts model to treat the matrix as the discrete lattices for simulating the grain growth and there will be a number of lattices to be computed synchronously. The scheme is performed by CUDA GPU parallelization programming framework which is of much more feasibility and low cost comparing with the former conventional program. The most key point is that the parallel algorithm is of great temporal performance which means it takes less time to complete a simulation. The results of comparative experiments show that the algorithm is unquestionable effective while the other statistic numerical features of simulations are almost the same.

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Periodical:

Advanced Materials Research (Volumes 332-334)

Pages:

1868-1871

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.332-334.1868

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Online since:

September 2011

Authors:

Xun Luo, Wei Zhao, Bao Shun Liu, Yan Ming Zhang

Keywords:

Cuda Parallelization Programming, Monte-Carlo Simulation, Parallel Algorithm, Templated Grain Growth

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References

[1] M. P. Anderson, D. J. Srolovitz, G. S. Grest and P. S. Sahni: Acta Metall. Vol. 32 (1984), p.783.

Google Scholar

[2] D. J. Srolovitz, M. P. Anderson, P.S. Sahni and G. S. Grest: Acta Metall. Vol. 32 (1984), p.793.

Google Scholar

[3] D. J. Srolovitz, M. P. Anderson, G. S. Grest and P. S. Sahni: Acta Metall. Vol. 32 (1984), P.1429.

Google Scholar

[4] D. J. Srolovitz, G. S. Grest and M. P. Anderson: Acta Metall. Vol. 34 (1986), p.1833.

Google Scholar

[5] Y. T. Liu, X. J. Guan, X. M. Shen, X. F. Ma and L. J. Wang: Acta Metall. Sin. (Engl. Lett.) Vol. 21 (2008), P.282.

Google Scholar

[6] H. Chen, C. Huang, H. Liu and B. Zou: Comput. Mater. Sci. Vol 47 (2009), p.326.

Google Scholar

[7] Q. Yu, M. Nosonovsky and S. K. Esche: Int. J. Mech. Sci. Vol. 51 (2009), p.434.

Google Scholar

[8] Chaohui Zhang: Application of Computer in Materials Science and Engineering (Central South University Press, China 2008, In Chinese).

Google Scholar

[9] NVIDIA CUDATM: NVIDIA CUDA C Programming Guide. http://www.nvidia.com/object/cuda_gpus.html.

DOI: 10.5626/jok.2015.42.12.1467

Google Scholar