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A Lagrangian Inertial Centroidal Voronoi Particle method for dynamic load balancing in particle-based simulations

机译:一种拉格朗日惯性质心Voronoi粒子载量动态负荷平衡粒子方法

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In this paper we develop a Lagrangian Inertial Centroidal Voronoi Particle (LICVP) method to extend the original CVP method (Fu et al., 2017) to dynamic load balancing in particle-based simulations. Two new concepts are proposed to address the additional problems encountered in repartitioning the system. First, a background velocity is introduced to transport Voronoi particles according to the local fluid field, which facilitates data reuse and lower data redistribution cost during rebalancing. Second, in order to handle problems with skew-aligned computational load and large void space, we develop an inertial-based partitioning strategy, where the inertial matrix is utilized to characterize the load distribution, and to confine the motion of Voronoi particles dynamically adapting to the physical simulation. Intensive numerical tests in fluid dynamics simulations reveal that the underlying LICVP method improves the incremental property remarkably without sacrifices on other objectives, i.e. the inter-processor communication is optimized simultaneously, and the repartitioning procedure is highly efficient. (C) 2019 Elsevier B.V. All rights reserved.
机译:在本文中,我们开发了一种拉格朗日惯性心性Voronoi粒子(LICVP)方法,以扩展原始CVP方法(Fu等,2017),以粒子基模拟中的动态负载平衡。建议两个新概念解决在重新分区系统时遇到的额外问题。首先,引入背景速度以传送根据局部流体场的voronoi颗粒,这有利于在重新平衡期间促进数据重用和降低数据再分配成本。其次,为了处理偏斜对齐的计算负荷和大的空隙空间的问题,我们开发了一种基于惯性的分区策略,其中惯性矩阵用于表征负载分布,并限制了voronoi颗粒动态适应的运动物理模拟。流体动力学模拟中的密集数值揭示了底层的LicVP方法显着改善了增量性质,而不是牺牲其他目标,即处理器间通信同时进行优化,并且重新分区过程是高效的。 (c)2019年Elsevier B.V.保留所有权利。

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