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.

人们对大型计算机系统功耗的日益关注使得研究重点转向相应的测量方法。不同的工作负载模式和能源效率优化导致当今计算节点的高度动态功耗，这对每个测量基础设施来说都是一个挑战。我们确定了表征此类基础设施的五个部分矛盾的要求：时间粒度、空间粒度、明确定义的准确性、可扩展性和成本。在两个项目中，我们突破了这些标准的界限：紧密集成到 HPC 系统中的数百个节点的毫秒粒度可扩展测量解决方案，以及复杂的单节点仪器，用于测量微秒范围内应用程序事件的功耗。两种测量解决方案都经过校准，并且仔细研究了它们的精度。我们讨论了大型系统中全球监控测量的可扩展处理，并将这些数据与应用程序性能跟踪数据等上下文信息结合起来用于能源效率分析。