1-Synchronous Programming of Large Scale, Multi-Periodic Real-Time Applications with Functional Degrees of Freedom
Bibliographic record
Abstract
The design and implementation of reactive, hard real-time systems involves modeling and generating efficient code for the integration of harmonic multi-periodic tasks. Such a reactive system can be modeled as a synchronous program orchestrating computations, state machine transitions and communications. In a harmonic multi-periodic integration program, task execution rates are related through integral ratios. This paper aims at providing a scalable way to implement large systems composed of modular, synchronous reactive tasks, and to generate efficient code satisfying real-time constraints.The paper describes three incremental extensions to the Lustre language and evaluates them on production applications. First, we propose a clock calculus for 1-synchronous clocks, i.e. strictly periodic clocks with a single activation on their period; we show how the compiler can exploit this information to raise the level of abstraction when integrating tasks at the system level. Second, we allow some variables to have unknown phases, extending the clock inference to gather constraints on unknown phases, using a solver for load balancing over multi-periodic real-time schedules, before instantiating this solution to assign clocks to all reactions of the system. Third, we propose temporally underspecified operations, relevant to many discrete control scenarii, for example on variables with low temporal variability; we show how to express this in a composable way, retaining the Kahn semantics of the synchronous program outside these controlled relaxations, and exploiting slack in the computation to relax the constraints of the real-time load-balancing problem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".