Analysis of Probabilistic Cache Related Pre-emption Delays
Bibliographic record
Abstract
This paper integrates analysis of probabilistic cache related pre-emption delays (pCRPD) and static probabilistic timing analysis (SPTA) for multipath programs running on a hardware platform that uses an evict-on-miss random cache replacement policy. The SPTA computes an upper bound on the probabilistic worst-case execution time (pWCET) of the program, which is an exceedance function giving the probability that the execution time of the program will exceed any given value on any particular run. The pCRPD analysis determines the maximum effect of a pre-emption on the pWCET. The integration between SPTA and pCRPD updates the pWCET to account for the effects of one or more pre-emptions at any arbitrary points in the program. This integration is a necessary step enabling effective schedulability analysis for probabilistic hard real-time systems that use pre-emptive or co-operative scheduling. The analysis is illustrated via a number of benchmark programs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".