Bibliographic record
Abstract
Overly optimistic processing in Time Warp can threaten the stability of the simulation due to large memory consump-tion and explosive rollback growth. To address the sta-bility concerns of optimistic simulation, Choe and Trop-per proposed a learning-based flow control algorithm which throttles over-optimistic execution by regulating the flow of events between pairs of processors throughout the course of the simulation. This flow control algorithm has been shown to effectively improve simulation stability for certain appli-cations in a shared-memory environment. In this paper we present an analysis and experimental ver-ification of the performance of this flow control algorithm in a distributed-memory environment. Results show that the flow control algorithm reduces the memory usage, the number of rollbacks and the number of antievents at the ex-pense of the simulation time. Thus it becomes apparent that the behaviour of the flow control algorithm is not a conse-quence of learning, but it is highly dependent on the type of simulation platform, event granularity and communica-tion latency. Taking these results into account, we discuss a number of approaches to learning and flow control using the outlines of the flow control algorithm, and we consider the extent of the performance improvement to be expected from memory-based schemes for limiting Time Warp opti-mism in a distributed-memory environment. 1
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".