Functional Parallelization of a Land Surface Model in Regional Climate Modeling
Bibliographic record
Abstract
Parallel computing is a very useful tool for computing intensive and time constrained real timeproblems. Depending on the size of the grid and processors available in the cluster, a group of nodes or processesin the grid can be represented by an individual processor and it can be responsible for their computational needs.This increases the accuracy of the model by allowing finer grid sizes, also leading to savings in time. Our study,utilizes the Canadian Land Surface Scheme (CLASS), a well-tested serial general land/atmosphere interactionmodel. CLASS is a vertical one-dimensional model and spatially adjacent nodes in the grid do not interact. Thismodel computes heat and moisture fluxes for bare ground (fractional coverage by ground), ground covered withsnow (fractional coverage by snow), ground with canopy (fractional coverage by ground), and ground with bothsnow and canopy. Within each spatial grid cell, these fractions are combined. In this paper, we demonstrate theneed of parallelizing the serial CLASS model and discuss the designs to implement it. This will enable finer gridsizes leading to higher accuracy of the model and a corresponding decrease in individual processor computingtime, when compared to the serial CLASS model. It was observed that a serial farm kind of design suits ourdesign constraints and has been successfully implemented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".