The XTREM power and performance simulator for the Intel XScale core
Bibliographic record
Abstract
Managing power concerns in microprocessors has become a pressing research problem across the domains of computer architecture, CAD, and compilers. As a result, several parameterized cycle-level power simulators have been introduced. While these simulators can be quite useful for microarchitectural studies, their generality limits how accurate they can be for any one chip family. Furthermore, their hardware focus means that they do not explicitly enable studying the interaction of different software layers, such as Java applications and their underlying runtime system software. This paper describes and evaluates XTREM, a power-simulation tool tailored for the Intel XScale microarchitecture. In building XTREM, our goals were to develop a microarchitecture simulator that, while still offering size parameterizations for cache and other structures, more accurately reflected a realistic processor pipeline. We present a detailed set of validations based on multimeter power measurements and hardware performance counter sampling. XTREM exhibits an average performance error of only 6.5% and an even smaller average power error: 4%. The paper goes on to present an application study enabled by the simulator. Namely, we use XTREM to produce an energy consumption breakdown for Java CDC and CLDC applications. Our simulator measurements indicate that a large percentage of the total energy consumption (up to 35%) is devoted to the virtual machine's support functions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".