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An application-centric characterization of domain-based SFC partitioners for parallel SAMR

机译:针对并行SAMR的基于域的SFC分区程序的以应用程序为中心的表征

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Structured adaptive mesh refinement (SAMR) methods for the numerical solution of partial differential equations yield highly advantageous ratios for cost/accuracy as compared to methods based on static uniform approximations. These techniques are being effectively used in many domains including computational fluid dynamics, numerical relativity, astrophysics, subsurface modeling, and oil reservoir simulation. Distributed implementations of these methods, however, lead to significant challenges in dynamic data-distribution, load-balancing, and runtime management. This paper presents an application-centric characterization of a suite of dynamic domain-based inverse space-filling curve partitioning techniques for the distributed adaptive grid hierarchies that underlie SAMR applications. The overall goal of this research is to formulate policies required to drive a dynamically adaptive metapartitioner for SAMR grid hierarchies capable of selecting the most appropriate partitioning strategy at runtime based on current application and system state. Such a metapartitioner can significantly reduce the execution time of SAMR applications.
机译:与基于静态均匀逼近的方法相比,用于偏微分方程数值解的结构化自适应网格优化(SAMR)方法在成本/精度方面产生了非常有利的比率。这些技术已在许多领域得到有效使用,包括计算流体力学,数值相对论,天体物理学,地下建模和油藏模拟。但是,这些方法的分布式实现在动态数据分布,负载平衡和运行时管理方面带来了重大挑战。本文介绍了以应用程序为中心的一组动态特性,这些特性是基于动态域的逆空间填充曲线分区技术的,用于基于SAMR应用程序的分布式自适应网格层次结构。这项研究的总体目标是为SAMR网格层次结构制定驱动动态自适应元分区程序所需的策略,这些层次结构能够根据当前应用程序和系统状态在运行时选择最合适的分区策略。这样的元分区程序可以大大减少SAMR应用程序的执行时间。

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