Load balancing using Hilbert space-filling curves for parallel shallow water simulations
Bibliographic record
Abstract
Представлен метод балансировки нагрузки вычислений с использованием кривых Гильберта применительно к параллельному алгоритму решения уравнений мелкой воды. Рассматриваемая система уравнений мелкой воды возникает в сигма-модели общей циркуляции океана INMOM (Institute of Numerical Mathematics Ocean Model) при разрешении гравитационных волн и является одним из основных блоков модели. Из-за наличия в океанах островов и берегов балансировка нагрузки вычислений на процессоры является особенно актуальной задачей. В качестве одного из таких методов был выбран метод балансировки нагрузки вычислений с использованием кривых Гильберта. Продемонстрирована большая эффективность этого метода по сравнению с равномерным разбиением без балансировки нагрузки и показано, что этот метод служит хорошей альтернативой библиотеке разбиений METIS. Оптимальность реализованного разбиения для мелкой воды точно соответствует оптимальности и для трехмерной сигма-модели INMOM в силу одинакового количества вертикальных уровней во всей расчетной области. This paper presents a method of load balancing using Hilbert space-filling curves applied to a parallel algorithm for solving shallow water equations. We consider the system of shallow water equations in the form presented in the ocean general circulation sigma-model INMOM (Institute of Numerical Mathematics Ocean Model). This system of equations is one of the basic blocks of the model. Due to land points in the computational grid, the load balancing is an especially urgent task. The method of load balancing using Hilbert space-filling curves is chosen as one of such methods. The paper demonstrates the greater efficiency of this method in comparison with the uniform partitioning without load balancing. It is shown that this method is a good alternative to the METIS standard library. Moreover, the optimality of the implemented partition for the shallow water equations exactly corresponds to the optimality for the INMOM three-dimensional sigma-model due to the same number of vertical levels in the entire computational domain.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".