Performance and configuration of hierarchical ring networks for multiprocessors
Bibliographic record
Abstract
Analytical queueing network models for expected message delay in 2-level and 3-level hierarchical-ring interconnection networks (INs) are developed. Such networks have recently been used in commercial and research prototype multiprocessors. A major class of traffic carried by these INs consists of cache line transfers, and associated coherency control messages, between processor caches and remote memory modules in shared-memory multiprocessors. Memory modules are assumed to be evenly distributed over the processor nodes. Such traffic consists of short, fixed-length messages. They can be conveniently transported using the slotted ring transmission technique, which is studied here. The message delay results derived from the models are shown to be quite accurate when checked against a simulation study. The comparisons to simulations include heavy traffic situations where queueing delays in ring crossover switches are significant for ring utilization levels of 80 to 90%. As well as facilitating analysis, the analytical models can be used to determine optimal sizes for the rings at different levels in the hierarchy under specified traffic distributions in a system with a given total number of processor nodes. Optimality is in terms of minimizing average message delay. A specific example of such a design exercise is provided for the uniform traffic case.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".