Xây dựng tô-pô mạng liên kết 3 chiều dựa trên kiến trúc DSN nhằm thích ứng cài đặt thực tế
Bibliographic record
Abstract
The highly rising demands today in high performance computing or building big data centers make most of traditional interconnection network topologies become outdated. Currently, to develop new types of high performance network, random topologies such as using random shortcuts, small-world models, are promising research directions. In this paper, we develop a new 3D topology named 3D-DSN based on the principles of Distributed Shortcut Network (DSN) [1] to support saving cable length. The main idea is to use a 2D-Torus as the base structure instead of a 1D-Ring as in [1]. Furthermore, the set of shortcuts are distributed in both directions of 2D-Torus and the routing logic is extended to 5 phases instead of 3 phases as in [1]. We also give the analyses of topology properties in both theoretical and experiment aspects, then conclude that 3D-DSN is more realistic than the basic DSN and provide a nice trade-off between network diameter (or average path length) and cable cost.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".