The rising concern for power consumption of large-scale computer systems puts a research focus on the respective measurement methods. Varying workload patterns and energy efficiency optimizations cause highly dynamic power consumption on today’s compute nodes—a challenge for every measurement infrastructure. We identify five partly contradictory requirements that characterize such infrastructures: temporal granularity, spatial granularity, well-defined accuracy, scalability, and cost. In two projects we push the boundaries for these criteria: a scalable measurement solution for hundreds of nodes at millisecond granularity that is tightly integrated into the HPC system, and a sophisticated single-node instrumentation to measure the power consumption of application events in the microsecond range. Both measurement solutions are calibrated and their accuracy is carefully studied. We discuss scalable processing of the measurements for global monitoring in large-scale systems and use this data for energy efficiency analyses in combination with contextual information such as application performance trace data.

Accurate and fine-grained power measurements of computing systems are essential for energy-aware performance optimizations of HPC systems and applications. Although cluster wide instrumentation options are available, fine spatial granularity and temporal resolution are not supported by the system vendors and extra hardware is needed to capture the power consumption information. We introduce the High Definition Energy Efficiency Monitoring (HDEEM) infrastructure, a sophisticated approach towards systemwide and fine-grained power measurements that enable energy- aware performance optimizations of parallel codes. Our approach is targeted at instrumenting multiple HPC racks with power sensors that have a sampling rate of about 8 kSa/s as well as finer spatial granularity, e.g., for per-CPU measurements. We specifically focus on the correctness of power measurement samples and energy consumption calculations based on these power samples. We also discuss scalable and low-overhead or overhead-free options for online and offline (postmortem) processing of power measurement data.