Partitioning and Selection of Data Consistency Mechanisms for Multicore Real-Time Systems
Why this work is in the frame
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Bibliographic record
Abstract
Multicore platforms are becoming increasingly popular in real-time systems. One of the major challenges in designing multicore real-time systems is ensuring consistent and timely access to shared resources. Lock-based protection mechanisms such as MPCP and MSRP have been proposed to guarantee mutually exclusive access in multicore systems at the expense of blocking. In this article, we consider partitioning and scheduling in multicore real-time systems with resource sharing. We first propose a resource-aware task partitioning algorithm for systems with lock-based protection. Wait-free methods, which ensure consistent access to shared memory resources with negligible blocking at the expense of additional memory space, are a suitable alternative when the shared resource is a communication buffer. We propose several approaches to solve the joint problem of task partitioning and the selection of a data consistency mechanism (lock-based or wait-free). The problem is first formulated as an Integer Linear Programming (ILP). For large systems where an ILP solution is not scalable, we propose two heuristic algorithms. Experimental results compare the effectiveness of the proposed approaches in finding schedulable systems with low memory cost and show how the use of wait-free methods can significantly improve schedulability.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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