高性能计算系统中日志数据分析的整体方法：以IBM Blue Gene/Q为例
A Holistic Approach to Log Data Analysis in High-Performance Computing Systems: The Case of IBM Blue Gene/Q
摘要：
管理高性能计算基础设施的复杂性和成本正在上升。通过预测模型实现自动化管理和修复，以最大限度地减少人为干预，是提高系统可用性并控制这些成本的一种尝试。构建足够准确以用于自动管理的预测模型不能基于来自子系统的受限日志数据，而是需要对来自不同来源的数据进行整体分析。在这里，我们提供了一个详细的多尺度表征研究的基础上，四个数据集报告功耗，温度，工作负载，和硬件/软件事件的IBM蓝色基因/Q安装。我们表明，该系统运行丰富的并行工作负载，其组件之间的温度和功率的相关性低，但较高的相关性的事件。正如预期的那样，功率和温度密切相关，而事件显示与负载和功率的负相关性。功率和工作负载显示出适度的相关性，并且仅在组件的规模上。该研究的目的是系统的，综合的表征计算基础设施和相关性的来源和水平的发现，作为未来的预测建模工作的基础。
英文摘要：
The complexity and cost of managing high-performance computing infrastructures are on the rise. Automating management and repair through predictive models to minimize human interventions is an attempt to increase system availability and contain these costs. Building predictive models that are accurate enough to be useful in automatic management cannot be based on restricted log data from subsystems but requires a holistic approach to data analysis from disparate sources. Here we provide a detailed multi-scale characterization study based on four datasets reporting power consumption, temperature, workload, and hardware/software events for an IBM Blue Gene/Q installation. We show that the system runs a rich parallel workload, with low correlation among its components in terms of temperature and power, but higher correlation in terms of events. As expected, power and temperature correlate strongly, while events display negative correlations with load and power. Power and workload show moderate correlations, and only at the scale of components. The aim of the study is a systematic, integrated characterization of the computing infrastructure and discovery of correlation sources and levels to serve as basis for future predictive modeling efforts.
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