Automatic Construction of Predictable and High-Performance Cache Coherence Protocols for Multicore Real-Time Systems
Bibliographic record
Abstract
Predictable hardware cache coherence is a viable shared data communication mechanism between cores for multicore real-time platforms. Prior works have established that predictable hardware cache coherence protocols offer significant performance advantages over alternative predictable data communication mechanisms while ensuring predictability. Unlike alternative predictable data communication mechanisms, designing predictable cache coherence protocols is nontrivial as it requires detailed understanding of the impact of different memory activity patterns to shared data for predictable and coherent data communication. Furthermore, designing predictable cache coherence protocols that deliver high average-case performance is even more challenging as it entails identifying opportunities such that a core’s access to a data is not stalled in the presence of interleaving memory activity from other cores to the same data. To this end, we present SYNTHIA, an open and automated tool for synthesizing predictable and high-performance snooping bus-based cache coherence protocols for multicore platforms deployed in real-time systems. SYNTHIA automates the complex analysis associated with designing predictable and high-performance cache coherence protocols, and constructs the complete protocol implementation (coherence states and transitions) that achieve predictability and performance. We use SYNTHIA to construct complete protocol implementations from simple specifications of common protocols (modified-shared-invalid (MSI), MESI, and MOESI protocols) and a predictable variant of the MESIF cache coherence protocol, which was recently found to be deployed in an existing multicore platform designed for real-time platforms. We validated the correctness, predictability, and performance guarantees of the generated protocol implementations from SYNTHIA using manually implemented versions, and a micro-architectural simulator.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".