A platform for parallel CFD FEM computations
Bibliographic record
Abstract
It is a time consuming and very skilful task for researchers or developers in computational mechanics to modify a program, designed for a single processor, to one suitable for parallel computation. This is a serious bottleneck in parallel computation, even though a general-purpose parallel computational library, such as MPI, is applied to this modification. We have developed a parallel matrix solver platform, based on a domain decomposition method, for various numerical schemes such as the finite element method (FEM), the finite difference method and the finite volume method, to accelerate a smooth shift to the realm of parallel computation. Parallel software such as PETSc, Aztec, GEOFEM and ADVENTURE have already been developed, however these systems are more suitable for professionals in parallel computation and not valid for our purpose. In our platform, a user is merely required to call the platform at the stage of stiffness matrix calculation. GMRES and Bi-CGSTAB with several pre-conditioners are used as a basic matrix solver. The option of invoking a Lagrange-multiplier is also included. For partitioning, a fast graph generator for arbitrary elements and an interface with MeTis are provided. Our platform is valid for a variety of hardware, including a single processor based workstation, through the exchange of Makefilein. The effectiveness of our platform is evaluated with several examples in the area of finite element fluid dynamics in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".