Minimizing Internal Speedup for Performance Guaranteed Switches With Optical Fabrics
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Bibliographic record
Abstract
We consider traffic scheduling in an N times N packet switch with an optical switch fabric, where the fabric requires a reconfiguration overhead to change its switch configurations. To provide 100% throughput with bounded packet delay, a speedup in the switch fabric is necessary to compensate for both the reconfiguration overhead and the inefficiency of the scheduling algorithm. In order to reduce the implementation cost of the switch, we aim at minimizing the required speedup for a given packet delay bound. Conventional Birkhoff-von Neumann traffic matrix decomposition requires N2 - 2N + 2 configurations in the schedule, which lead to a very large packet delay bound. The existing DOUBLE algorithm requires a fixed number of only 2N configurations, but it cannot adjust its schedule according to different switch parameters. In this paper, we first design a generic approach to decompose a traffic matrix into an arbitrary number of Ns (N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - 2N + 2 > N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> > N) configurations. Then, by taking the reconfiguration overhead into account, we formulate a speedup function. Minimizing the speedup function results in an efficient scheduling algorithm ADAPT. We further observe that the algorithmic efficiency of ADAPT can be improved by better utilizing the switch bandwidth. This leads to a more efficient algorithm SRF (scheduling residue first). ADAPT and SRF can automatically adjust the number of configurations in a schedule according to different switch parameters. We show that both algorithms outperform the existing DOUBLE algorithm.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it