Power awareness through selective dynamically optimized traces
Bibliographic record
Abstract
We present the PARROT concept that seeks to achieve higher performance with reduced energy consumption through gradual optimization of frequently executed code traces. The PARROT microarchitectural framework integrates trace caching, dynamic optimizations and pipeline decoupling. We employ a selective approach for applying complex mechanisms only upon the most frequently used traces to maximize the performance gain at any given power constraint, thus attaining finer control of tradeoffs between performance and power awareness. We show that the PARROT based microarchitecture can improve the performance of aggressively designed processors by providing the means to improve the utilization of their more elaborate resources. At the same time, rigorous selection of traces prior to storage and optimization provides the key to attenuating increases in the power budget. For resource-constrained designs, PARROT based architectures deliver better performance (up to an average 16% increase in IPC) at a comparable energy level, whereas the conventional path to a similar performance improvement consumes an average 70% more energy. Meanwhile, for those designs which can tolerate a higher power budget, PARROT gracefully scales up to use additional execution resources in a uniformly efficient manner. In particular, a PARROT-style doubly-wide machine delivers an average 45% IPC improvement while actually improving the cubic-MIPS-per-WATT power awareness metric by over 50%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".