Multi-criteria Graph Partitioning with Scotch
Why this work is in the frame
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Bibliographic record
Abstract
Load balancing parallel multi-physics simulations is a hard task often performed by solving a multi-criteria partitioning problem. The aim of this paper is to describe how this problem is solved in Scotch, explaining the various algorithmic choices performed. We also present a method to generate multi-criteria weight distributions for meshes corresponding to those obtained by Monte-Carlo particle transport simulations. This method is used on 5 meshes that serve to compare multi-criteria partitioning tools. A mesh corresponding to an industrial test case is also considered. In order to compare multi-criteria partitioning tools, we analyze their performance profiles. Results show that Scotch returns solutions of smaller edgecut than other partitioning tools such as MeTiS and PaToH, especially for the industrial test case.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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