Bibliographic record
Abstract
The history of the Time Warp protocol, one of the most commonly used synchronization protocols in parallel and distributed simulation, has been a history of optimizations. Usually the optimization problems are solved by creating an analytical model for the simulation system through careful analysis of the behavior of Time Warp. The model is expressed as a closed-form function that maps system state variables to a control parameter. At run-time, the system state variables are measured and then utilized to derive the value for the control parameter. This approach makes the quality of the optimization heavily dependent on how closely the model actually reflects the simulation reality. Because of the simplifications that are necessary to make in the course of creating such models, it is not certain the control strategies are optimal. Furthermore, models based on a specific application cannot be readily adopted for other applications. In this thesis, as an alternative approach, we present a number of model-free direct-search algorithms based on techniques from system control, machine learning, and evolutionary computing, namely, learning automata, reinforcement learning, and genetic algorithms. What these methods have in common is the notion of learning. Unlike the traditional methods used in Time Warp optimization, these learning methods treat the Time Warp simulator as a black box. They start with a set of candidate solutions for the optimization parameter and try to find the best solution through a trial-and-error process: learning automata give a better solution a higher probability to be tried; reinforcement learning keeps a value for each candidate that reflects the candidate's quality; genetic algorithms have a dynamic set of candidates and improves the quality of the set by mimicking the evolutionary process. We describe how some optimization problems in Time Warp can be transformed into a search problem, and how the learning methods can be utilized to directly search for the optimal value for the system control parameter. Compared with the analytical model-based approach, these methods are more generic in nature. Since the search is based on actual run-time performance of different values for the control parameter, the learning methods also better reflect the simulation reality.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".