Two Modified Multicast Algorithms for Two Dimensional Mesh and Torus Networks
Bibliographic record
Abstract
Multicast is message propagation from a source node to a number of destination nodes. Multicast could become the bottleneck for the performance of processes and systems. The main parameters for evaluating a multicast are time and traffic. Optimizing multicast time and traffic is proven to be NP-hard. In this paper, we propose improvements on two tree based multicast algorithms, PAIR and DIAG, in 2D mesh and torus networks. We prove that MDIAG generates optimal or optimal plus one multicast time in 2D meshes. MDIAG has lower complexity than DIAG, but might generate more traffic. MPAIR generates the least traffic, has the same complexity as PAIR and better complexity than DIAG, but might generate more time. The average time generated by both algorithms in a 2D torus is almost half the average time generated in a 2D mesh for the same algorithm. The average traffic generated by both algorithms in a 2D torus is less than the average traffic generated in a 2D mesh in most cases for the same algorithm. Extensive simulations of the algorithms show that 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".