Predictable Cache Coherence for Multi-core Real-Time Systems
Why this work is in the frame
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Bibliographic record
Abstract
This work addresses the challenge of allowing simultaneous and predictable accesses to shared data on multicore systems. We propose a predictable cache coherence protocol, which mandates the use of certain invariants to ensure predictability. In particular, we enforce these invariants by augmenting the classic modify-share-invalid (MSI) protocol with transient coherence states, and minimal architectural changes. This allows us to derive worst-case latency bounds on predictable MSI (PMSI) protocol. Our analysis shows that while the arbitration latency scales linearly, the coherence latency scales quadratically with the number of cores, which emphasizes that importance of accounting for cache coherence effects on latency bounds. We implement PMSI in gem5, and execute SPLASH-2 and synthetic workloads. Results show that our approach is always within the analytical worst-case latency bounds, and that PMSI improves averagecase performance by up to 4 over the next best predictable alternative. PMSI has average slowdowns of 1.45 and 1.46 compared to MSI and MESI protocols, respectively.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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