The multi-GPU Wetland DEM Ponding Model
Bibliographic record
Abstract
The Wetland DEM (Digital Elevation Model) Ponding Model ( WDPM ) is software that simulates how runoff water is distributed across the Canadian Prairies. Previous versions of the WDPM are able to run in parallel with a single CPU or GPU. Now that multi-device parallel computing has become an established method to increase computational throughput and efficiency, this study extends WDPM to a multi-GPU parallel algorithm with efficient data transmission methods via overlapping communication with computation. The new implementation is evaluated from several perspectives. First, the output summary and system are compared with the previous implementation to verify correctness and demonstrate convergence. Second, the multi-GPU code is profiled, showing that the algorithm carries out efficient data synchronization through optimized techniques. Finally, the new implementation was tested experimentally and showed improved performance and good scaling. Specifically, a speedup of 2.39 was achieved when using four GPUs compared to using one GPU. • WDPM simulates how runoff water is distributed across the Canadian Prairies. • WDPM has been generalized to take advantage of multiple GPUs. • A speedup of 2.39 is observed using 4 GPUs compared to 1 GPU on a real dataset. • By using leading computing paradigms, WDPM can perform difficult simulations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".