An architecture-centric approach to the development of a distributed model-checker for timed automata
Bibliographic record
Abstract
Research in Model-Checking is focused on increasing the size of the problems tools can deal with. The ultimate wave has been the use of Distributed-Computing, where a cluster of computers work together to solve the problem [8, 3, 9].In our work we present a distributed model-checker that evolves from the tool Kronos [5] and can handle backwards computation of TCTL-reachability formulae [1] over timed-automata [2]. Our proposal, including the arguments of its correctness, is based on software architectures, using a notation adapted from [6]. We find such an approach a natural and general way to address the development of complex tools that need to incorporate new features and optimizations as they evolve.We introduce some interesting features such as a priori graph partitioning (using METIS [7], a standard library for graph partitioning), a sophisticated machinery to reach optimum performance (communication piggybacking and delayed messaging) and dead-time utilization, where every processor uses time intervals of inactivity to perform auxiliary, time-consuming tasks that will later speed up the rest of the computation.The correctness proof strategy combines an architecture evolution with the theoretical results about fix point calculation developed by Patrick Cousot in 1978 [4].
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".