Efficient Multicast Algorithms for Mesh and Torus Networks
Bibliographic record
Abstract
With the increasing popularity of multicomputers, efficient way of communication within its processors is a popular area of research. Multicomputers refer to a computer system that has multiple processors, they have high computational power and they can perform multiple tasks concurrently. Mesh and Torus are some of the commonly used network topologies in building multicomputer systems. Their performance highly depends on the underlying network communication such as multicast. Multicast is a communication method in which a message is sent from a source node to a certain number of destinations. Two major parameters used to evaluate multicast are time that a multicast process takes to deliver the message to all destinations and traffic that indicates the number of links used for this process. Research indicates that in general, it is NP-complete to find an optimal multicasting algorithm which is efficient on both time and traffic. This paper suggests two new algorithms to achieve multicast in mesh and torus networks. Extensive simulations of these algorithms show that in practice they perform better than existing ones.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".