Parallel Processing Enhancement to SWMM/EXTRAN
Bibliographic record
Abstract
Modifications have been made to the FORTRAN source code of the EXTRAN block of SW1viM, which enable the model to take advantage of parallel processors for faster program execution during runtime.These modifications have been made to the program code which performs the explicit (Modified Euler) solution of the St. Venant equations for computation of flow and head within the modeled drainage network.The code changes are designed to support use of OpenMP (see Bibliography) parallel processing directives when the code is compiled using specialized parallel processing compiler extensions (KAP/Pro Toolset for Opelli\1P).Code changes were verified for correct parallclization and model output confirmed by testing against output produced with the serial (unmodified) version of the same source code.Model output and runtimes were characterized for two relatively large model networks (386 and 772 conduits) by executing the serial and parallelized code on the same hardware (Windows® NT workstation running dual Pentium® 200 MHz microprocessors).Runtime reductions on the order of 30-37% were found for the paralle1ized code on this commonly available dual processor system.The modified code and Open:MP support an unlimited number of parallel processors, and greater runtime reductions are expected for more highly parallel systems (e.g.those with four or more processors).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".