177PARALLELIZATION OF FVCOM PARALLELIZATION OF THE FVCOM COASTAL OCEAN MODEL
Bibliographic record
Abstract
The Finite Volume Coastal Ocean Model (FVCOM) is a publicly available software package for simulation of ocean processes in coastal areas. The unstructured grid approach used in the model is highly advantageous for resolving dynamics in regions with complex shorelines such as estu-aries, embayments, and archipelagos. A growing user community and a demand for large-scale, high resolution simulations has driven the need for the implementation of a portable and efficient parallelization of the FVCOM core code. The triangular grid approach used in FVCOM pre-cludes the utilization of schemes used previously in the parallelization of popular structured grid ocean models. This paper describes recent work on a SPMD paralleli-zation of FVCOM. The METIS partitioning libraries are employed to decompose the domain. Parallel operations are programmed with the Message Passing Interface (MPI) standard interface. Updates for flow quantities near the interprocessor domain boundaries are performed using a mixture of halo and flux summation approaches to mini-mize communication overhead. Evaluation of the imple-mentation efficiency is made on machines comprising several parallel architectures and interconnect types. The implementation is found to scale well on medium-sized ( ~ 256 processor) clusters. An execution time model is developed to expose bottlenecks and extrapolate the per-formance of FVCOM to increasingly available large MPP machines. Application to a model of water circulation in the Gulf of Maine shows that the parallelized code greatly increases the capabilities of the original core scheme by extending practical model simulation timescales and spa-tial resolution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".