Power-performance trade-offs for energy-efficient architectures: A quantitative study
Bibliographic record
Abstract
The drastic increase in power consumption by modern processors emphasizes the need for power-performance trade-offs in architecture design space exploration and compiler optimizations. This paper reports a quantitative study on the power-performance trade-offs in software pipelined schedules for an Itanium-like EPIC architecture with dual-speed pipelines, in which functional units are partitioned into fast ones and slow ones. We have developed an integer linear programming formulation to capture the power-performance tradeoffs for software pipelined loops. The proposed integer linear programming formulation and its solution method have been implemented and tested on a set of SPEC2000 benchmarks. The results are compared with an Itanium-like architecture (baseline) in which there are four functional units (FUs) and all of them are fast units. Our quantitative study reveals that by introducing a few slow FUs in place of fast FUs in the baseline architecture, the total energy consumed by FUs can be considerably reduced. When 2 out of 4 FUs are set as slow, the total energy consumed by FUs is reduced by up to 31.1% (with an average reduction of 25.2%) compared with the baseline configuration, while the performance degradation caused by using slow FUs is small. If performance demand is less critical, then energy reduction of up to 40.3% compared with the baseline configuration can be achieved.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".