A Simulation-based Model Generator for Software Performance Estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the rise of software system complexity, developers rely more on a modular approach to system design to reduce development cost. However, as a result, integrating a real-time system becomes a challenge. To be able to properly integrate the system, software developers are required to provide software characteristics such as the execution times of their components to ensure the correct timing behaviour of the overall system. Generally, engineers rely on profilers available on their workstations to collect execution times of software. Yet, the final target architecture is usually vastly different from that of the workstation. Furthermore, the fact that the target platform is mostly inaccessible at design time calls for tools that can estimate the execution time of components on a wide range of architectures with reasonable cost. In this paper, we propose a methodology that relies on (1) fast simulation techniques and (2) analytical tools that build predictive models to estimate the execution times of components on a target architecture with minimum detail. We show that the approach is able to predict the execution times of a set of benchmarks when migrated from a reference architecture to a target platform with comparable accuracy to simulation, while being 2 orders of magnitude faster.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it