Development and performance optimization of a parallelcomputing infrastructure for an unstructured-meshmodelling framework
Bibliographic record
Abstract
Abstract. This paper describes the development and performance optimization of a parallel computing infrastructure for an unstructured-mesh global model (GRIST; Global-to-Regional Integrated forecast SysTem). The focus is on three major aspects that facilitate rapid iterative development, including parallel computing, index optimization and an efficient group I/O strategy. For parallel computing, the METIS tool is used for the partition of the global mesh, which is flexible and convenient for both the quasi-uniform and variable-resolution simulations. The scaling tests show that the partition method is efficient. To improve the cache efficiency, several mesh index reordering strategies are investigated to optimize the performance of the indirect addressing scheme used in the stencil calculations. The numerical results show that the indexing strategies are able to speed up the calculations, especially for running with a small number of processes. To overcome the bottleneck of poor I/O efficiency for the high-resolution or massively parallel simulations, a group parallel I/O method is implemented and proven to be of high efficiency in the numerical experiments. Altogether, these three aspects of the parallel computing toolkits are encapsulated in a few interfaces, which can be used for general parallel modelling on unstructured meshes.
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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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".