An algorithm for threading assignment in large-scale wireless network mobile simulations
Bibliographic record
Abstract
When using parallel computing to run large-scale simulations, the parts of the system being simulated in different cores or threads often interact and exchange information, constraining the threads to be synchronized. Simulating wireless networks with mobility, when a user equipment (UE) ceases to be served by one Base Station (BS), to be served by a new one, a synchronization point may be required, if the new BS is being simulated in another thread. In a large-scale distributed wireless network with high mobility, the simulation speed-up obtained from multi-threading could be lost to the overhead burden for synchronizing the threads. We propose a heuristic approach to assign BSs to threads in such a way as to minimize the number of synchronization points. In a time interval of the simulation, accumulated interactions are interpreted as growing graphs. Advancing through the simulation time until the number of disconnected graphs is equal to the number of desired threads, showed to be a good strategy to determine the longest intervals that can be simulated without synchronization points while taking advantage of multi-threading. By means of simulation tests we show decrements of up to 100.0 %, in the number of synchronization points, in comparison to those required for the same simulation times when assigning BSs to threads in a random and balanced way.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".