CML-PowF: Data Clustering Matching Based Low-overhead Multiple CPU Real-time Power Forecasting

Efficient CPU power capping is essential for energy saving and fault tolerance in high-performance computing (HPC) clusters, but its effectiveness depends on accurate and timely processor power forecasting with minimal sampling overhead. Existing methods often struggle to balance these factors under scalability constraints, as hardware limitations tightly bound the available sampling resources. This paper focuses on the issue of high-precision real-time processor power forecasting while maintaining (or minimally increasing) the total overhead of multiprocessor power forecasting, particularly when the parallelism scale ranges from P to 2 P processors or when the problem size scales from M to 2 M . We propose CML-PowF , a low-overhead multiprocessor real-time power forecasting approach based on data clustering. CML-PowF integrates two key algorithms: Alg-CEF , which conducts cluster matching on the runtime characteristics of the program at the P / M scale, and models the trade-off among forecasting error, time span, and sampling overhead. Alg-MSF , which leverages execution patterns from smaller-scale runs to determine the optimal sampling overhead and forecasting time span at the 2 P /2 M scale. We evaluate CML-PowF on x86 and ARM platforms with up to 32 computing nodes (2048 cores). Results show that it achieves 3-6% forecasting error at large scales with only 0.2-0.5% degradation compared to the P / M scale, without increasing total sampling overhead. Integrated with the PowC control system, CML-PowF effectively maintains real-time processor power below target thresholds.