Tracking Coherence-Related Contention Delays in Real-Time Multicore Systems
Bibliographic record
Abstract
The prevailing use of multicores in Embedded Critical Systems (ECS) is multi-application workloads in which independent applications run in different cores with data sharing restricted to the communication between applications and the real-time operating system. However, thread-level parallelism is increasingly used, e.g., OpenMP, in ECS to improve individual applications' performance. At the hardware level, we are witnessing increased research efforts to master and improve multicore cache coherence that plays a key role enabling efficient data sharing among threads. Despite these efforts, the limited information provided by performance monitoring counters on cache coherence limits the understanding of coherence's impact on tasks execution time and hence, poses severe constraints to estimate tight worst-case execution time bounds. In this line, this work contributes with an analysis of the impact that cache coherence can have on application timing behavior, and a new set of low-overhead performance monitoring counters that can be used to track the coherence-related contention that different threads can cause on each other when sharing data. Our results show that the proposed performance monitoring counters effectively capture all coherence-related contention that tasks can suffer and hence are key for parallel software timing validation and verification in ECS. Furthermore, they help application optimization by providing key information about data sharing among the application threads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".