Bibliographic record
Abstract
The Alya System is the BSC simulation code for multi-physics problems [1]. It is based on a Variational Multiscale Finite Element Method for unstructured meshes. Work distribution is achieved by partitioning the original mesh into subdomains (submeshes). This pre-partition step has until now been done in serial by only one process, using the metis library [2]. This is a huge bottleneck when larger meshes with millions of elements have to be partitioned. This is due to the data not fitting in the memory of a single computing node and in the cases where the data does fit; Alya takes too long in the partitioning step. In this document we explain the tasks done to design, implement and test a new parallel partitioning algorithm for Alya. In this algorithm a subset of the workers, is in charge of partition the mesh in parallel, using the parmetis library [3]. Partitioning workers, load consecutive parts of the main mesh, with a parallel space partitioning bin structure [4], capable of obtaining the adjacent boundary elements of their respective submeshes. With this local mesh, each of the partitioning workers is able to create its local element adjacency graph and to partition the mesh. We have validated our new algorithm using a Navier-Stokes problem on a small cube mesh of 1000 elements. Then we performed a scalability test on a 30M element mesh to check if the time to partition the mesh is reduced proportionally with the number of partitioning workers. We have also done a comparison between metis and parmetis, the balancing of the element distribution among the domains, to test how the use of many partitioning workers to partition the mesh affects the scalability of Alya. We have noticed in these tests that it’s better to use fewer partitioning workers to partition the mesh. Finally we have two sections explaining the results and the future work that has to be done in order to finalise and improve the parallel partition algorithm.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.440 | 0.359 |
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".