Timesteps and Parallel Domain Decomposition with Application to Astrophysical Simulations
Bibliographic record
Abstract
Many modelling applications target systems with a broad range of dynamical timescales. If only a fraction of the modelled system requires small timesteps, large speedups in processing can be achieved by integrating each part of the system with a local timestep. In the astrophysical simulation example taken here with a range of 214 in timesteps, speed-ups over a single global timestep of a factor of 10 have been achieved in parallel with a particle tree-code. In this regime assumptions about dominant costs and ideal load balancing schemes derived from analysis of single stepping simulations break down. In particular, book-keeping and data management tasks can overtake scientific calculation costs. This work examines a new approach based on associating data with similar timesteps rather than using locality in simulation space to control processing costs and to improve load balance and scalability in parallel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".