Sequential Performance of Asynchronous Conservative PDES Algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The widespread use of sequential simulation in large scale parameter studies means that large cost savings can be made by improving the performance of these simulators. Sequential discrete event simulation systems usually employ a central event list to manage future events. This is a priority queue ordered by event timestamps. Many different priority queue algorithms have been developed with the aim of improving simulator performance. Researchers developing asynchronous conservative parallel discrete event simulations have reported exceptional performance for their systems running sequentially in certain cases. This paper compares the performance of simulations using a selection of high performance central event list implementations to that achieved using techniques borrowed from the parallel simulation community. Theoretical and empirical analysis of the algorithms is presented demonstrating the range of performance that can be achieved, and the benefits of employing parallel simulation techniques in a sequential execution environment.
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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 | 0.000 | 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 it