Providing differentiated services, congestion management, and deadlock freedom in dragonfly networks with adaptive routing
Bibliographic record
Abstract
Summary The number of endnodes in high‐performance computing systems has grown significantly in the last years. Hence, the interconnection network has become an essential issue as it may end up being the system bottleneck if it is not properly designed. In that sense, the Dragonfly topology has become very popular for interconnecting high‐performance computing systems in the last years because it offers high performance at an affordable cost. However, when using deterministic minimal‐path routing, this topology is not able to offer a high performance under certain traffic conditions. This problem can be solved by using oblivious or adaptive routing. However, there are no congestion management techniques specially tailored to Dragonfly topologies using oblivious or adaptive routing. Note that in congestion situations, the Dragonfly performance may drop because of the head‐of‐line blocking effect. This effect could be even more dangerous in systems where several applications with different priorities coexist. In this work we propose several techniques especially designed for providing differentiated services and congestion management in Dragonfly networks using oblivious or adaptive routing. First, we propose thehierarchical 3‐level queuingqueuing scheme, which configures several virtual channels distributed into 3 virtual networks to reduce the head‐of‐line blocking while deadlocks derived from the routing algorithm are prevented. Second, we extendhierarchical 3‐level queuingto provide differentiated services through 2 different solutions. Finally, some experiments are performed to show the benefits obtained by using the proposed techniques.
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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 | 0.000 | 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 | 0.000 |
| 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".