A modern solver interface to manage solution algorithms in the Community Earth System Model
Bibliographic record
Abstract
Global Earth System Models (ESMs) can now produce simulations that resolve ~50 km features and include finer scale, interacting physical processes. However, the current explicit algorithms that dominate production ESMs require ever-decreasing time steps in order to achieve these fine-resolution solutions, which limits time to solution even when efficiently exploiting the spatial parallelism. Solution methods that overcome these bottlenecks can be quite intricate, and there is no single set of algorithms that perform well across the range of problems of interest. This creates significant implementation challenges, which is further compounded by the complexity of ESMs. Therefore, prototyping and evaluating new algorithms in these models requires a software interface that is flexible, extensible, and easily introduced into the existing software. We describe our efforts to create a parallel solver interface that links the Trilinos collection of solver libraries to the Glimmer Community Ice Sheet Model (Glimmer-CISM), a continental ice-sheet model used in the Community Earth System Model (CESM). We demonstrate this interface within both current and developmental versions of Glimmer-CISM and provide strategies for its integration into the rest of the CESM.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".