Preparing TELEMAC-2D for extremely large simulations
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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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it