Parallel implementation of hierarchical tetrahedral - octahedral (HTO) subdivision for 3-D finite element mesh refinement
Bibliographic record
Abstract
Parallel computing is being used more and more frequently in 3-D finite element (FE) mesh generation in electromagnetics, due to its improvements in efficiency. When applying parallel computing, the computational problem usually needs to be broken into discrete pieces, so that it can be solved simultaneously with multiple compute resources. Less time is then required than with a single compute resource. In this thesis, an algorithm for hierarchical tetrahedral---octahedral (HTO) subdivision was studied and implemented with a parallel message passing interface (MPI). The data structure was designed in such a way as to store the geometric data during the mesh computation. Also, broadcasting and data gathering was used to build up the final geometric file. The experimental results and the enhancement of system performance are presented, comparing sequential computing with parallel computing. The program was implemented in C language/MPI, and the results obtained have made use of the CLUMEQ1 supercomputer Centre facilities at McGill University. 1CLUMEQ stands for Consortium Laval UQAM McGill and Eastern Quebec for high performance computing.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".