Power Modeling for Heterogeneous Processors
Bibliographic record
Abstract
As power becomes an ever more important design consideration, there is a need for accurate power models at all stages of the design process. While power models are available for CPUs and GPUs, only simple models are available for heterogeneous processors. We present a micro-benchmark-based modeling technique that can be used for chip multiprocessor (CMPs) and accelerated processing units (APUs). We use our approach to model power on an Intel Xeon CPU and an AMD Fusion heterogeneous processor. The resulting error rate for the Xeon's model is below 3% and is only 7% for the Fusion. We also present a method to reduce the number of benchmarks required to create these models. Instead of running micro-benchmarks for every combination of factors (e.g. different operations or memory access patterns), we cluster similar micro-benchmarks to avoid unnecessary simulations. We show that it is possible to eliminate as many as 93% of the compute micro-benchmarks, while still producing power models having less than 10% error rate.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".