Global Lookahead Management (GLM) Protocol for Conservative DEVS Simulation
Bibliographic record
Abstract
An approach to carrying out asynchronous distributed simulation of multiprocessor message passing architectures is presented. Aiming at achieving better performance on Conservative DEVS-based simulations, we introduce the GLM protocol which borrows the idea of safe processing intervals from the conservative time window algorithm and maintains global synchronization in a fashion similar to the distributed snapshot technique. Under the GLM scheme, a central look ahead manager (LM) exists which is in charge of receiving every LP's look ahead, identifying the global minimum look ahead of the system, and broadcasting it via null messages to all LPs. The simulation is divided into cycles of two phases: Parallel phase and Broadcast phase. The GLM protocol is asynchronous and the central look ahead manager is not expected to be a bottleneck since the only message transmissions involving it take place when all LPs are blocked waiting for permission to advance their LVTs. The results presented in this paper show that the GLM protocol not only significantly reduces the total number of null messages, but it improves the performance and higher speedups are achieved.
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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 | 0.000 |
| 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) | 0.002 | 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".