Data-Driven Power Modeling and Monitoring via Hardware Performance Counter Tracking
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arXiv
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| Format: | Preprint |
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2025
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| author | Mazzola, Sergio Ara, Gabriele Benz, Thomas Forsberg, Björn Cucinotta, Tommaso Benini, Luca |
| author_facet | Mazzola, Sergio Ara, Gabriele Benz, Thomas Forsberg, Björn Cucinotta, Tommaso Benini, Luca |
| contents | Energy-centric design is paramount in the current embedded computing era: use cases require increasingly high performance at an affordable power budget, often under real-time constraints. Hardware heterogeneity and parallelism help address the efficiency challenge, but greatly complicate online power consumption assessments, which are essential for dynamic hardware and software stack adaptations. We introduce a novel power modeling methodology with state-of-the-art accuracy, low overhead, and high responsiveness, whose implementation does not rely on microarchitectural details. Our methodology identifies the Performance Monitoring Counters (PMCs) with the highest linear correlation to the power consumption of each hardware sub-system, for each Dynamic Voltage and Frequency Scaling (DVFS) state. The individual, simple models are composed into a complete model that effectively describes the power consumption of the whole system, achieving high accuracy and low overhead. Our evaluation reports an average estimation error of 7.5% for power consumption and 1.3% for energy. We integrate these models in the Linux kernel with Runmeter, an open-source, PMC-based monitoring framework. Runmeter manages PMC sampling and processing, enabling the execution of our power models at runtime. With a worst-case time overhead of only 0.7%, Runmeter provides responsive and accurate power measurements directly in the kernel. This information can be employed for actuation policies in workload-aware DVFS and power-aware, closed-loop task scheduling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23672 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Power Modeling and Monitoring via Hardware Performance Counter Tracking Mazzola, Sergio Ara, Gabriele Benz, Thomas Forsberg, Björn Cucinotta, Tommaso Benini, Luca Performance Hardware Architecture Energy-centric design is paramount in the current embedded computing era: use cases require increasingly high performance at an affordable power budget, often under real-time constraints. Hardware heterogeneity and parallelism help address the efficiency challenge, but greatly complicate online power consumption assessments, which are essential for dynamic hardware and software stack adaptations. We introduce a novel power modeling methodology with state-of-the-art accuracy, low overhead, and high responsiveness, whose implementation does not rely on microarchitectural details. Our methodology identifies the Performance Monitoring Counters (PMCs) with the highest linear correlation to the power consumption of each hardware sub-system, for each Dynamic Voltage and Frequency Scaling (DVFS) state. The individual, simple models are composed into a complete model that effectively describes the power consumption of the whole system, achieving high accuracy and low overhead. Our evaluation reports an average estimation error of 7.5% for power consumption and 1.3% for energy. We integrate these models in the Linux kernel with Runmeter, an open-source, PMC-based monitoring framework. Runmeter manages PMC sampling and processing, enabling the execution of our power models at runtime. With a worst-case time overhead of only 0.7%, Runmeter provides responsive and accurate power measurements directly in the kernel. This information can be employed for actuation policies in workload-aware DVFS and power-aware, closed-loop task scheduling. |
| title | Data-Driven Power Modeling and Monitoring via Hardware Performance Counter Tracking |
| topic | Performance Hardware Architecture |
| url | https://arxiv.org/abs/2506.23672 |