HIERARCHICAL DOMAIN DECOMPOSITION WITH PARALLEL MESH REFINEMENT FOR BILLIONS-OF-DOF SCALE FINITE ELEMENT ANALYSES
Bibliographic record
Abstract
This paper describes a parallel fast generation method of large-scale meshes for a hierarchical domain decomposition method implemented in the open source parallel finite element software ADVENTURE. Since large-scale meshes need to be generated in order to perform various analyses in Japan's Petaflops Supercomputer, nicknamed the "K computer", a mesh refinement function and a communication table generation function without communication are newly developed and implemented for the hierarchical domain decomposition tool named ADVENTURE_Metis. The developed new version is named ADVENTURE_Metis Ver.2. Since a generation cost of a communication table for sending and receiving data among computational nodes becomes so expensive for the refined large-scale mesh, the present authors have newly developed a parallel algorithm such that the communication tables of vertices, edges and faces are updated each other during mesh refinement after the initial communication tables of vertices, edges and faces are generated for an initial mesh. As a result, the generation of a refined mesh model over billions degrees of freedom (DOFs) from an initial medium-size mesh model of about a million DOFs can be performed in a parallel computer in a short time.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".