Metis-CIC: A new mesh partitioning heuristic for parallel preconditioned iterative methods in CFD
Bibliographic record
Abstract
Mesh partitioning has a significant influence on the efficiency of parallel computational fluid dynamics (CFD). This paper considers the most widely-used preconditioned conjugated gradient (PCG) methods for solving the linear systems in parallel since it is the core and most time-consuming part in CFD. Based on the analysis of how mesh partitioning impacts the solution time of parallel PCG, we propose a new cost function combining communication overhead and iterative convergence rate together. An adjustable parameter is involved in the cost function. For a specific application and a given parallel degree, the parameter can be fitted using profiling information of the application. A partitioning method based on the new cost function, named Metis-CIC (Communication overhead and Iterative convergence rate Combined), is implemented in Metis (a general graph partitioning tool). Numerical results of two CFD applications show that Metis-CIC outperforms the standard Metis partitioning routine and Metis-Acut which only considers the iterative convergence rate during the partitioning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".