Pore Networks Simulation with Parallel Greedy Algorithms
Bibliographic record
Abstract
Porous media simulation is an important contribution in the study of many physical phenomena. The No MISS greedy algorithm outstands from the existing sequential algorithms for constructing a pore sub network, in a relatively fast way. However, despite the No MISS time reduction, there are still problems related to the required processing time when very large networks need to be studied. In this work, a non scalable parallel version of the No MISS algorithm is presented, and a new approach is proposed to alleviate this issue, in both versions cluster cores work simultaneously on different porous sub network spaces. The first approach, named as Unbounded-No MISS, allows the cores to go forward with the initialization of the porous sub network space, applying a balancing policy when a core needs more data. At the end, the cores require a sequential synchronization to finish the porous network construction. The second approach, named as Bounded-No MISS, controls the porous sub network initialization by considering a site-size boundary, avoiding the final strong synchronization and improving considerably the scalability. The obtained results using a 125-core cluster are presented.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".