Parallel processing of excavation in soils with randomly generated material properties
Bibliographic record
Abstract
Today, affordable high performance clusters enable engineers to carry out large nonlinear 3D analyses very quickly using parallel processing. It has been estimated that by 2018, a 1 Petaflop cluster (with 100,000 cores) will cost around $150,000; a price affordable by a reasonably sized engineering firm or University department. With these significant improvements in technology, it is becoming increasingly cost-effective to incorporate uncertainty into analyses, for example by undertaking Monte Carlo simulations with randomly generated soil properties. As each realisation is independent, it can be executed at the same time. For large models that require move memory than is available on a single core, each realisation can be solved by subdividing the problem over multiple cores. The authors will present results for an excavation problem using this two-level parallelisation strategy. The parallel software used, ParaFEM, has been recently updated to interface with a number of external tools including the RFEM library, the visualisation tool ParaView and the graph partitioner METIS. For a single realisation, ParaFEM can make good use of 32,000 cores and solve problems with 1 billion degrees of freedom.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".