Head‐of‐line blocking avoidance in Slim Fly networks using deadlock‐free non‐minimal and adaptive routing
Bibliographic record
Abstract
Summary Interconnection network performance is a key issue in HPC systems and datacenters, especially as their number of end nodes grows, to cope with application needs. The network topology and the routing algorithm are important factors for performance and cost. Topologies such as fat‐tree or Dragonfly were proposed to maximize network performance while reducing network resources. One of the most promising topologies is Slim Fly, which offers high network bandwidth assuring low network diameter. However, adversarial traffic and/or congestion situations may degrade Slim Fly's performance dramatically. Non‐minimal routings, such as Valiant or UGAL, can mitigate the former problem while queuing schemes can handle the latter one. In this paper, we proposed a combined mechanism to provide Slim Fly network with both non‐minimal routing and queuing schemes by using several virtual networks to guarantee deadlock freedom. Each virtual network consists of a set of virtual channels to store packets separately according to a mapping policy. This diminishes the interaction among traffic flows, thus reducing head‐of‐line blocking. The results obtained from a simulation‐based evaluation show that our proposal enhances the performance in all the traffic cases, in contrast to other mechanisms whose performance drops in certain scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".