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Ensuring Domain Consistency in an Adaptive Framework with Distributed Topology for Fluid Flow Simulations

机译:在具有分布式拓扑的流体流动模拟自适应框架中确保域一致性

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Top-tier parallel computing clusters continue to accumulate more and more computational power with more and better CPUs and Networks. This allows, especially for environmental simulations, computations with larger domain sizes and better resolution. One of the challenges becoming increasingly important is the decomposition and distribution of the overall work load. State-of-the-art parallel codes usually use solutions that involve complete knowledge of the domain topology, which will lead to communication and memory bottlenecks when computing very large domains. To meet this challenge, the authors propose a new strategy for decentralised domain management, based on a proven hierarchic data structure. On the way of developing a framework where individual sub-domains only have local knowledge of their surroundings, this contribution describes the communication patterns used in ensuring a consistent domain, without the need for expensive global broadcast messages. Furthermore, the routines necessary to deal with adaptive changes in domain topology, due to refinement, coarsening and migration of sub-domains to different computational resources, are discussed in detail.
机译:顶级并行计算集群通过越来越多的更好的CPU和网络不断积累越来越多的计算能力。特别是对于环境仿真,这允许使用更大的域大小和更好的分辨率进行计算。越来越重要的挑战之一是整体工作量的分解和分配。最新的并行代码通常使用的解决方案涉及领域拓扑的完整知识,这在计算非常大的域时会导致通信和内存瓶颈。为了应对这一挑战,作者提出了一种基于成熟的分层数据结构的分散域管理新策略。在开发单个子域仅对其周围环境具有本地知识的框架的方式上,此贡献描述了用于确保一致域而无需昂贵的全局广播消息的通信模式。此外,详细讨论了由于子域的细化,粗化和向不同计算资源的迁移而导致的域拓扑自适应变化所必需的例程。

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