Efficient CPU power capping is essential for energy saving and fault tolerance in parallel computing 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 article 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 2P processors or when the problem size scales from M to 2M . 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 tradeoff 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 2P / 2M scale. We evaluate CML-PowF on x86 and ARM platforms with up to 32 computing nodes (2,048 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.

The high accuracy of processor power consumption modeling has consistently posed challenges in processor design and program power optimization. With the increasing complexity of processor architectures and the diversification of application types, enhancing the accuracy of processor power models has become increasingly difficult. In addition to model selection and feature selection for modeling parameters, the quantity and distribution of training set samples significantly affect the improvement of processor model accuracy. To reduce model complexity, high-dimensional feature spaces are often subjected to dimensionality reduction. However, this can sometimes lead to bias in sample point clustering within the feature space (fuzzy clustering), thereby impacting the accuracy of processor power models. Addressing this issue, this paper proposes a novel method called "FIFO: Fuzzy Cluster Identification and Feature Optimization for Processor Power Modeling Sample Optimization". The FIFO algorithm optimizes the distribution of training sample points for processor power models by implementing fuzzy cluster identification in low-dimensional feature space (FI), high-dimensional feature space restoration and clustering optimization (FO), and redundant point elimination based on mixed-dimension feature spaces, thereby enhancing the accuracy of processor modeling. Validation of the FIFO algorithm’s effectiveness was conducted through linear power modeling and neural network power modeling on both x86 and ARM processor platforms. Experimental results demonstrate that employing the FIFO algorithm reduces processor power model errors by an average of 13.69% on an ARMv8-based architecture processor platform, by 15.76% on the Intel Xeon Gold-6226R processor platform, and by 25.41% on the Intel Xeon E5-2660 processor platform.

In an era where power and energy are the first-class constraints of computing systems, accurate power information is crucial for energy efficiency optimization in parallel computing systems. Existing power monitoring techniques rely on either software-centric power models that suffer from poor accuracy or integrated hardware measurement schemes that have a low reading update frequency and coarse granularity. These result in a low spatiotemporal resolution for power monitoring. This paper introduces HighRPM, a new method for accurately measuring power consumption on parallel computing systems. HighRPM combines coarse-grained power sensor readings and software power modeling techniques to improve temporal and spatial resolutions. To provide high-frequent power readings in the temporal domain, HighRPM employs statistical modeling and machine learning techniques to predict the long-term power trend and the short-term fluctuations in power consumption. To improve spatial coverage, HighRPM takes low-time resolution node-level power consumption and uses a neural network to distribute the power readings to lower-level computing components like CPUs and memory components. We evaluate HighRPM by applying it to both ARM-based and X86-based platforms. Experimental results show that HighRPM improves time resolution by 10 times, provides accurate readings for CPUs and memory, and reduces error by 7-24% compared to other power modeling methods.