Thermal-Aware Power Migration in Many-Core Processors
Bibliographic record
Abstract
The demand for greater performance in applications involving high levels of parallelism and sequential computation, has led to an increase in design complexity, has rendered the single-core processor obsolete for such applications and resulted in more cores being put onto a single chip. While improving performance, this has lead to increased power densities and, consequently, increased die temperature. Also, the power distribution across the die surface is not uniform, resulting in hot spots. The increase in die temperature results in decreased performance and reliability and increased leakage currents and cooling costs. Spreading activity across a multi-core chip is increasingly being considered as a way to contain chip temperatures while minimally degrading performance. This paper investigates power migration, or “core hopping,” which involves dynamic allocation of workload among the cores on a many-core processor. This work numerically analyzed core hopping for different configurations of a many-core processor, and performed the migration based on both time of activity and temperature of individual cores. Based on the analysis, this work demonstrated a notable drop in junction temperature of about 8°C.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".