Development of Stencil-Based Mesh Partitioning for Parallel Unstructured CFD Solvers
Bibliographic record
Abstract
A study evaluating unstructured mesh partitioning for computational uid dynamics (CFD) simulations on parallel computers is presented. Considerations for eectiv e partitioning of computational unstructured meshes for a family of implicit time-integration methods using the line-relaxation algorithm are outlined. Mesh partitioning using the Metis library is evaluated for two- and three-dimensional meshes. Dieren t combinations of computational stencil information provided to the partitioning library are outlined and comparison of the resulted load balance and communication volume measures are presented. An augmentation to the existing mesh partitioning method via a heuristic algorithm is proposed. The heuristic algorithm relies on an initial partitioning of the mesh, which is then rened iteratively. Results using the new method are compared with the standard partitioning methods. This is done both from a theoretical perspective and by running the CFD code. Elapsed time results of the CFD code are presented from simulations on a parallel computer. Results show that the improved partitioning using the stencil information and the heuristic algorithm result in faster turn-around times. The stencil-based partitioning and the proposed heuristic algorithm are simple modications to existing codes and are applicable to general simulations employing domain decomposition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".