Profiling and Understanding CPU Power Management in Linux
Bibliographic record
Abstract
Dynamic Voltage and Frequency Scaling (DVFS) is a popular technique for power management. Understanding the DVFS principles and the current practice of existing DVFS governors embedded in operating systems (OS) is essential. This work aims to provide a deep understanding of DVFS power management through real-time profiling on various Linux platforms, including an Intel-based laptop, an ARM-based Jetson Nano Board, and a Raspberry Pi platform. We first present the theoretical model for dynamic and static power consumption and describe experiments on three platforms to show how frequency affects their power consumption. We then visualize the real-time behaviour of the existing DVFS governors: the Ondemand and Conservative governors in OS kernels on multiple platforms under different parameter settings. Furthermore, we identify issues that may be encountered in actual hardware experiments but are often overlooked by researchers. Examples are coarse-grained frequency levels and the OS interface not reflecting the actual frequency. Finally, we design experiments to explore the relationship between utilization and power. This work can help DVFS algorithm designers to consider the practical aspect of integrating DVFS algorithms into actual systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".