Adaptive Power Profiling for Many-Core HPC Architectures

State of the art schedulers use workload profiles to help determine which resources to allocate. Traditionally, threads execute on every available core, but increasingly, too much power is consumed by using every core. Because peak power can occur at any point in time during the workload, workloads are commonly profiled to completion multiple times in an offline architecture. In practice, this process is too time consuming for online profiling and alternate approaches are used, such as profiling for k% of the workload or predicting peak power from similar workloads. We studied the effectiveness of these methods for core scaling. Core scaling is a technique which executes threads on a subset of available cores, allowing unused cores to enter low-power operating modes. Schedulers can use core scaling to reduce peak power, but must have an accurate profile across potential settings for number of active cores in order to know when to make this decision. We devised an accurate, fast and adaptive approach to profile peak power under core scaling. Our approach uses short profiling runs to collect instantaneous power traces for a workload under each core scaling setting. The duration of profiling varies for each power trace and depends on the desired accuracy. Compared to k% profiling of peak power, our approach reduced the profiling duration by up to 93% while keeping accuracy within 3%.