Parallel Simulation of Pore Networks Using Multicore CPUs
Bibliographic record
Abstract
Pore networks can be simulated in silico by using the dual site-bond Model. In this approach, a set of cavities (sites) are interconnected to each other by means of a set of throats (bonds), while considering that each site should be always larger than any of its delimiting bonds. The NoMISS greedy algorithm has been implemented recently in order to address this task; nevertheless, even if this procedure is relatively fast, there arises problems related to large memory consumption and long computing time, as pore networks become somewhat large. Here, three parallel methods are proposed to allow a proficient construction of large pore networks. The first method is a parallel Monte Carlo procedure, which applies a number of exchanges among pore sizes in order to obtain a valid pore network. The other two methods are parallel versions of the pioneering NoMISS greedy algorithm. The first version uses a static data partitioning to speed up the running time, whilst the second applies a dynamic data distribution policy to improve the pore network quality. The obtained results show the behavior of each proposed version with respect to their performance and quality, by employing the resources of a 125-core Linux cluster.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".