Performance evaluation of a next-generation CFD on various supercomputing systems
Performance evaluation of a next-generation CFD on various supercomputing systems
复制标题
下一代 CFD 在各种超级计算系统上的性能评估
DOI：
10.1007/978-3-642-32454-3_11
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
H. Kobayashi
中科院分区：
文献类型：
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作者：
K. Komatsu;T. Soga;R. Egawa;H. Takizawa;H. Kobayashi
基于等间距笛卡尔网格的建筑立方体方法（Building-Cube Method， BCM）是一种新的CFD方法，可以在大型超级计算系统上进行高效的三维流动模拟。由于网格间距相等，流域可以划分为等分的单元，因此流计算可以划分为计算成本相同的部分计算。为了获得持续的高性能，考虑到超级计算系统特征的体系结构感知实现和优化是必不可少的，因为超级计算系统有各种类型，如标量类型、矢量类型和加速器类型。本文讨论了各种超级计算系统（如Intel Nehalem-EP集群、Intel Nehalem-EX集群、富士通FX-1、日立SR16000 M1、NEC SX-9和GPU集群）的架构感知实现和优化，并分析了它们在BCM中的持续性能。性能分析表明，内存和网络能力在很大程度上影响BCM的性能，而不是计算潜力。
The Building-Cube Method (BCM) has been proposed as a new CFD method for an efficient three-dimensional flow simulation on large-scale supercomputing systems, and is based on equally-spaced Cartesian meshes. As a flow domain can be divided into equally-partitioned cells due to the equally-spaced meshes, the flow computations can be divided to partial computations of the same computational cost. To achieve a high sustained performance, architecture-aware implementations and optimizations considering characteristics of supercomputing systems are essential because there have been various types of supercomputing systems such as a scalar type, a vector type, and an accelerator type. This paper discusses the architecture-aware implementations and optimizations for various supercomputing systems such as an Intel Nehalem-EP cluster, an Intel Nehalem-EX cluster, Fujitsu FX-1, Hitachi SR16000 M1, NEC SX-9, and a GPU cluster, and analyses their sustained performance for BCM. The performance analysis shows that memory and network capabilities largely affect the performance of BCM rather than computational potentials.