Parallel and distributed simulation: fast cell level ATM network simulation
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
This paper presents performance results for cell level ATM network simulations using both sequential and parallel discrete event simulation kernels. Five benchmarks are used to demonstrate the performance of the simulation kernels for different types of model. The results demonstrate that for the type of network models used in the benchmarks, the TasKit simulation kernel is able to outperform all of the other kernels tested both sequentially and in parallel. For one benchmark TasKit is shown to outperform a conventional sequential simulation kernel by a factor of 3. For the same benchmark TasKit is shown to outperform the best of the other parallel kernels tested by a factor of 6. The paper explains how this performance advantage is achieved and cautions that additional research into automatic model partitioning will be essential to make this technology accessible to the general simulation community.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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