CPU-GPU耦合算法在多孔介质系统多相流解模拟与分析中的千兆级应用

J. McClure, Hao Wang, J. Prins, Cass T. Miller, Wu-chun Feng
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引用次数: 18

摘要

大规模模拟可以为建立和验证多孔介质体系多相流理论模型提供广泛的信息。在本文中,我们考虑了一种耦合解决方案,其中多相流模拟器与用于提取流动演变界面几何形状的分析方法相耦合。这已经使用MPI来实现,目标是配备gpu的异构节点。gpu使用晶格玻尔兹曼方法对多相流解进行演化,而cpu则计算相分布的形态和拓扑及其演化速率的放大度量。我们的方法被证明可以扩展到4,096个gpu和65,536个CPU内核,在Titan上实现每秒244,754百万格节点更新(MLUPS)的双精度执行的最大性能。与以前的工作相比,这种方法增加了系统的大小,可以考虑一个数量级,并且可以在以前不可能的时间分辨率下对平均流量进行详细的原位跟踪。此外,它实际上消除了后处理和密集I/O的需要,并减轻了与节点故障相关的潜在数据丢失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Petascale Application of a Coupled CPU-GPU Algorithm for Simulation and Analysis of Multiphase Flow Solutions in Porous Medium Systems
Large-scale simulation can provide a wide range of information needed to develop and validate theoretical models for multiphase flow in porous medium systems. In this paper, we consider a coupled solution in which a multiphase flow simulator is coupled to an analysis approach used to extract the interfacial geometries as the flow evolves. This has been implemented using MPI to target heterogeneous nodes equipped with GPUs. The GPUs evolve the multiphase flow solution using the lattice Boltzmann method while the CPUs compute up scaled measures of the morphology and topology of the phase distributions and their rate of evolution. Our approach is demonstrated to scale to 4,096 GPUs and 65,536 CPU cores to achieve a maximum performance of 244,754 million-lattice-node updates per second (MLUPS) in double precision execution on Titan. In turn, this approach increases the size of systems that can be considered by an order of magnitude compared with previous work and enables detailed in situ tracking of averaged flow quantities at temporal resolutions that were previously impossible. Furthermore, it virtually eliminates the need for post-processing and intensive I/O and mitigates the potential loss of data associated with node failures.
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