Use of Predictive Performance Modeling during Large-scale System Installation
Use of Predictive Performance Modeling during Large-scale System Installation
复制标题
在大规模系统安装过程中使用预测性能建模
DOI：
10.1142/s0129626405002301
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
H. Wasserman
中科院分区：
文献类型：
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作者：
D. Kerbyson;A. Hoisie;H. Wasserman
在本文中，我们描述了一个重要的使用预测应用程序的性能建模-在一个新的大规模系统安装测量性能的验证。使用先前开发和验证的SAGE性能模型，SAGE是一种具有自适应网格细化的多维，3D，多材料流体动力学代码，我们能够帮助指导Los Alamos ASCI Q超级计算机第一阶段的稳定化。我们回顾了这个代码的分析模型的显着特点，已被应用于预测其性能的一大类万亿级并行系统。我们描述了在系统安装和升级过程中应用的方法，以建立可实现的“真实的”系统性能的基线。我们还显示了某些关键子系统，如PCI总线速度和多轨网络的整体应用性能的影响。我们表明，利用预测性能模型也是一个强大的系统调试工具。
In this paper we describe an important use of predictive application performance modeling - the validation of measured performance during a new large-scale system installation. Using a previously-developed and validated performance model for SAGE, a multidimensional, 3D, multi-material hydrodynamics code with adaptive mesh refinement, we were able to help guide the stabilization of the first phase of the Los Alamos ASCI Q supercomputer. We review the salient features of an analytical model for this code that has been applied to predict its performance on a large class of Tera-scale parallel systems. We describe the methodology applied during system installation and upgrades to establish a baseline for the achievable "real" performance of the system. We also show the effect on overall application performance of certain key subsystems such as PCI bus speed and multi-rail networks. We show that utilization of predictive performance models is also a powerful system debugging tool.