Preparing TELEMAC-2D for extremely large simulations
Bibliographic record
Abstract
This paper describes the latest developments that have been carried out to prepare TELEMAC-2D for simulations using grids composed of hundreds of millions of elements.Even running modest-sized simulations involving around 2 to 10 million grid elements highlights some critical issues concerning both the grid generation and the subsequent grid pre-processing which is currently handled by the PARTEL TELEMAC system tool.A serial accelerated global mesh refinement technique is presented which allows the generation of a 425-million element grid from an existing 106million element grid in less than an hour on a fat node of an IBM POWER7 cluster.The current version of PARTEL (version 6.0) relies on METIS 4.0 as the partitioner and has two main drawbacks for extremely large simulations; namely, METIS 4.0 is highly memory consuming, and secondly, PARTEL is extremely time-consuming when performing the rest of the pre-processing stage.Four alternative partitioners are tested on large grids, and a new parallel pre-processing tool, PARTEL_P, has been designed with the aim of optimising memory consumption.This new tool allows the pre-processing of a 200-million element grid on up to 32,768 sub-domains and its output has successfully been used to evaluate the scaling performance of TELEMAC-2D on an IBM Blue Gene/P.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".