Cooperative boosting
Bibliographic record
Abstract
This paper examines the interaction between thermal management techniques and power boosting in a state-of-the-art heterogeneous processor consisting of a set of CPU and GPU cores. We show that for classes of applications that utilize both the CPU and the GPU, modern boost algorithms that greedily seek to convert thermal headroom into performance can interact with thermal coupling effects between the CPU and the GPU to degrade performance. We first examine the causes of this behavior and explain the interaction between thermal coupling, performance coupling, and workload behavior. Then we propose a dynamic power-management approach called cooperative boosting (CB) to allocate power dynamically between CPU and GPU in a manner that balances thermal coupling against the needs of performance coupling to optimize performance under a given thermal constraint. Through real hardware-based measurements, we evaluate CB against a state-of-the-practice boost algorithm and show that overall application performance and power savings increase by 10% and 8% (up to 52% and 34%), respectively, resulting in average energy efficiency improvement of 25% (up to 76%) over a wide range of benchmarks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".