Bibliographic record
Abstract
This paper presents an extension of EDF feasibility analysis for tasks that invoke accelerators. For embedded systems with hard real-time deadlines, it is important to be able to verify that all of the tasks will meet their deadlines. A missed deadline could result in catastrophic behaviour of the system. Hence the importance of feasibility analysis of schedules. The scheduling policy to be analyzed in this paper is the Earliest Deadline First (EDF) policy. It is important in the realtime community because it makes optimal use of the processor. Also, using the deadline as a task's priority is a more natural way to specify the importance of tasks. EDF feasibility analysis is usually based on processor demand. The deadlines are sorted in chronological order. At each deadline, the demand on the processor since the starting point is analyzed to see if it exceeds the available processor time. The goal of this work is to extend and verify the feasibility analysis for systems in which hardware accelerators are used to speed up critical sections of the application. When a task uses an accelerator, it temporarily transfers the task's execution to another processor (the accelerator) which means that the main processor is available for use by a task of lower priority (later deadline). So the purpose of the extended feasibility analysis is to take into account the temporary parallelization of execution. For this work, a way to represent tasks using accelerators was developed and a new type of critical section was also defined. The critical section is used to extend processor demand analysis to tasks that use an accelerator during execution.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".