A Load-balancing Tool for Structured Multi-block CFD Applications Applied to a Parallel Newton-Krylov Algorithm
Bibliographic record
Abstract
For high-fidelity parallel computational fluid dynamic (CFD) simulations, multi-block grid methodology makes it possible to simulate flows around complex geometries. An automatic load-balancing tool is developed for a parallel Newton-Krylov algorithm that uses multi-block grids. The load-balancing tool uses a recursive edge bisection tool for splitting blocks to enforce load-balancing \nconstraints. When homogeneous multi-block grids are used, an optional constraint is introduced to control block splitting. For heterogeneous multi-block grids, a block size constraint prevents smaller blocks from being split when the tool is started of with a \nsmaller number of blocks than processors. The load-balancing tool is applied to three-dimensional multi-block grids for a Newton-Krylov solution process applied to the Euler \nand Reynolds-Averaged Navier-Stokes equations. For heterogeneous grids, significant reductions in turnaround time is obtained using the load-balancing tool than without a load-balancing tool. Finally, using the automatic tool, the scaling properties of the parallel Newton-Krylov algorithm are investigated.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".