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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".