Delay bound analysis for heterogeneous multicore systems using network calculus
Bibliographic record
Abstract
In heterogeneous multicore systems, a real challenging problem is to provide a timing performance guarantee for applications that have stringent timing constraints. One challenge is that task execution time varies. A dominant solution to this issue is to use the worst-case execution time as an upper bound. Another challenge arises from the communication delays due to task dependencies, which highly depend on task scheduling schemes. There lacks a formal approach to provide a rigorous analysis of transmission delays for heterogeneous multicore systems. In this paper, a mathematical tool, namely Network Calculus, is employed for end-to-end delay analysis. Under this framework, a virtual channel concept is first introduced for communication between CPU cores. Then a flow regulation model is proposed for network performance analysis. An upper bound of communication delays in a heterogeneous multicore system is established. A case study is provided to demonstrate the proposed approach. Based on a formal analysis, mathematical upper bounds of transmission delays are obtained to ensure the system performance.
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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 | 0.000 | 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 | 0.001 | 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".