Distributed re‐arrangement scheme for balancing computational load and minimizing communication delays in HLA‐based simulations
Bibliographic record
Abstract
SUMMARY Because of the availability of shared resources, substantial efforts have been applied to the development of large‐scale distributed simulations, and performance has become an essential aspect that can be impaired by heterogeneity and availability of resources, dynamic, unpredictable load imbalances, and communication delays. In order to manage and keep such distributed simulations consistent, the high level architecture (HLA) standard has been designed; however, it does not provide any solution that directly solves simulation performance issues. Many balancing approaches have been proposed in order to offer a suboptimal balancing solution, but they are limited to certain simulation aspects, are specific to determined applications, or are unaware of the HLA‐based simulation characteristics. In light of considering both computational and communication aspects for HLA‐based simulations, a centralized hierarchical balancing scheme was proposed. This scheme presents several drawbacks that make it susceptible to bottlenecks, overheads, global synchronization, and single point of failure. Therefore, a scheme based on a distributed algorithm to re‐arrange the computational and communication load is proposed. Experiments have been performed to evaluate the effectiveness of the distributed scheme when compared with the scheme based on a centralized redistribution algorithm. The results showed that the distributed balancing technique could provide similar performance gain or even improve it for some specific cases. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".