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The complexity and cost of managing high-performance computinginfrastructures are on the rise. Automating management and repair throughpredictive models to minimize human interventions is an attempt to increasesystem availability and contain these costs. Building predictive models thatare accurate enough to be useful in automatic management cannot be based onrestricted log data from subsystems but requires a holistic approach to dataanalysis from disparate sources. Here we provide a detailed multi-scalecharacterization study based on four datasets reporting power consumption,temperature, workload, and hardware/software events for an IBM Blue Gene/Qinstallation. We show that the system runs a rich parallel workload, with lowcorrelation among its components in terms of temperature and power, but highercorrelation in terms of events. As expected, power and temperature correlatestrongly, while events display negative correlations with load and power. Powerand workload show moderate correlations, and only at the scale of components.The aim of the study is a systematic, integrated characterization of thecomputing infrastructure and discovery of correlation sources and levels toserve as basis for future predictive modeling efforts.

A Holistic Approach to Log Data Analysis in High-Performance Computing Systems: The Case of IBM Blue Gene/Q