高性能计算Input/Output变异性预测及其在系统优化中的应用
Prediction of High-Performance Computing Input/Output Variability and Its Application to Optimization for System Configurations
摘要：
性能可变性是衡量高性能计算系统可靠性的重要指标。性能可变性受到许多因素之间复杂交互的影响，例如CPU频率、输入/输出（IO）线程数和IO调度程序。在本文中，我们专注于HPC IO可变性。预测HPC的可变性是一个具有挑战性的问题，在工程的HPC系统，有很少的统计工作，这个问题的日期。虽然有许多方法可在计算机实验文献中，现有的方法HPC性能变化的适用性需要调查，特别是，当目标是预测性能变化的内插和外推设置。一个数据分析框架的开发，从大规模的实验收集的数据建模。各种有前途的方法被用来建立预测模型的变化HPC系统。我们通过测量预测精度在以前看不见的系统配置的方法的性能进行评估。我们还讨论了优化系统配置的方法，使用估计的变异图。本文中的方法比较和开发的工具集的结果产生了新的见解现有的统计方法，可以是有益的HPC变异性管理的实践。本文在线提供补充材料。
英文摘要：
Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between numerous factors, such as CPU frequency, the number of input/output (IO) threads, and the IO scheduler. In this paper, we focus on HPC IO variability. The prediction of HPC variability is a challenging problem in the engineering of HPC systems and there is little statistical work on this problem to date. Although there are many methods available in the computer experiment literature, the applicability of existing methods to HPC performance variability needs investigation, especially, when the objective is to predict performance variability both in interpolation and extrapolation settings. A data analytic framework is developed to model data collected from large-scale experiments. Various promising methods are used to build predictive models for the variability of HPC systems. We evaluate the performance of the methods by measuring prediction accuracy at previously unseen system configurations. We also discuss a methodology for optimizing system configurations that uses the estimated variability map. The findings from method comparisons and developed tool sets in this paper yield new insights into existing statistical methods and can be beneficial for the practice of HPC variability management. This paper has supplementary materials online.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
