Memory efficient global scheduling of real-time tasks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current computing architectures are commonly built with multiple cores and a single shared main memory. Even though this architecture increases the overall computation power, main memory can easily become a bottleneck. Simultaneous access to main memory from multiple cores can cause both (1) severe degradation in performance and (2) unpredictable execution time for real-time applications. We propose in this paper to mitigate these two problems by co-scheduling cores as well as the main memory for predictable execution. In particular, we use a DMA component to overlap memory with computation for hiding the memory latency and therefore increasing the system performance. The main contribution of this paper is a novel global co-scheduling algorithm along with its associated schedulability analysis for sporadic hard real-time tasks. We evaluated our system by generating synthetic tasksets based on real benchmark parameters. The results show a significant improvement in system utilization while retaining a predictable system behavior.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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