Parallel Performance And Benchmarking Of The CE-QUAL-ICMFamily Of Three-dimensional Water Quality Models
Bibliographic record
Abstract
Accurate analyses of water quality issues pertaining to waterways require the application of eutrophication and contaminant transport/fate models to evaluate management alternatives. The movement of models to scalable, parallel computing platforms is a necessity since these simulations exhaust the resources of single processor computing systems. The CE-QUAL-ICM family of three-dimensional water quality models, developed at the U.S. Army Engineer Research and Development Center Waterways Experiment Station (WES), Vicksburg, MS, consists of an eutrophication model (ICM) and a transport/fate model (ICM/TOXI). Both codes were parallelized by combining a single program multiple data (SPMD) execution model with data domain decomposition using the message passing interface (MPI) library. Two different domain decomposition strategies were tested for performance, a Hilbert Space-Filling technique from the Center for Subsurface Modeling, University of Texas at Austin and the METIS multi-level graph partitioning package from the University of Minnesota. Evaluating the parallel versions included obtaining performance statistics on three platforms: IBM-SP, Cray T3E, and SGI Origin 2000. Results from the code parallelization effort indicate an order of magnitude decrease in model run-time can be achieved with as little as 16 processors. Furthermore, the application of these parallel codes to grids of varying resolution for the same test site indicate better performance can be obtained as the grid resolution increases.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".