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A GPU based iteration approach to efficiently evaluate radiation symmetry for laser driven inertial confinement fusion

机译:基于GPU的迭代方法可有效评估激光驱动惯性约束聚变的辐射对称性

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摘要

Radiation computation is very important for high energy density experiments design in the laser-driven Inertial Confinement Fusion. The view-factor based models are often used to calculate the radiation on the capsule inside a hohlraum. However, it usually takes much time to solve them when the number of equations is very large.In this paper, an efficient iteration approach GPU is presented. The core idea is: (1) guaranteed symmetry, strictly diagonally dominant, and positive definite properties underlying the models are described, (2) a preconditioned conjugate gradient iteration approach is presented to compute the radiation based on such guaranteed properties, and (3) such approach is then parallelized and implemented for GPU so that the large scale models, especially for the non-linear model, can be efficiently solved in reasonable time.Finally, two experimental targets for Shenguang laser facilities built in China are demonstrated and compared to validate the efficiency of the presented approach. The results show that, the models’ computation (1) can be speeded up with successive over-relax iteration method by eight times as compared with Cholesky factorization based direct approach, (2) can be accelerated more with the preconditioned conjugate gradient iteration approach by almost eight times, and (3) can be further accelerated about 2 to 4 times as it parallelized and run on the GPU, which enables the large scale models, can be efficiently solved in reasonable time on the usual desktop computers.
机译:辐射计算对于激光驱动惯性约束聚变中的高能量密度实验设计非常重要。基于视图因子的模型通常用于计算大黄蜂内膜囊上的辐射。但是,当方程数很大时,通常需要花费很多时间来求解它们。本文提出了一种有效的迭代方法GPU。核心思想是:(1)描述了模型基础上的保证对称性,严格对角占优和正定性质;(2)提出了一种预处理的共轭梯度迭代方法,基于这种保证性质来计算辐射;以及(3)然后将这种方法并行化并在GPU上实施,以便可以在合理的时间内有效地解决大规模模型(尤其是非线性模型)的问题。最后,对中国建造的神光激光设施的两个实验目标进行了演示和比较,以验证所提出方法的效率。结果表明,与基于Cholesky分解的直接方法相比,采用连续超松弛迭代方法可将模型的计算速度提高八倍;通过预处理的共轭梯度迭代方法,可将模型的计算速度进一步提高(2)。在并行化并在GPU上运行时,(3)可以进一步加速约2至4倍,从而可以在大型台式机上在合理的时间内有效地解决大型模型。

著录项

  • 来源
    《Applied Mathematical Modelling》 |2018年第7期|293-304|共12页
  • 作者单位

    Provincial Key Laboratory of Computer Integrated Manufacturing, Guangdong University of Technology;

    Provincial Key Laboratory of Computer Integrated Manufacturing, Guangdong University of Technology;

    Laser Fusion Research Center, China Academy of Engineering Physics;

    Laser Fusion Research Center, China Academy of Engineering Physics;

    Provincial Key Laboratory of Computer Integrated Manufacturing, Guangdong University of Technology;

    Laser Fusion Research Center, China Academy of Engineering Physics;

    Laser Fusion Research Center, China Academy of Engineering Physics;

    Provincial Key Laboratory of Computer Integrated Manufacturing, Guangdong University of Technology;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Laser Driven Inertial Confinement Fusion; iteration approach; GPU; preconditioned conjugate gradient;

    机译:激光驱动惯性约束融合;迭代方法;GPU;预处理共轭梯度;

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