Scalability Modeling For Deterministic Particle Transport Solvers
Bibliographic record
Abstract
In this paper, we propose a new parallel solver for the large-scale 3D neutron transport problems used in nuclear reactor simulations. Modern large-memory computers have made possible direct application of transport methods to large-scale computational models. However, many numerical acceleration techniques common to lattice transport codes are not applicable to heterogeneous geometry, especially high-dominance ratio when eigenvalue problems are solved. Consequently, large heterogeneous reactor problems have remained computationally intensive and impractical for routine engineering applications. Based on the characteristics method, this new code solves the transport equation by following neutron tracks. Due to the excessive number of these tracks in the demanding context of 3D large-scale calculations, the parallelization of the solver is the only way to obtain a fast solution. The parallelization is based on distributing a group of tracks, generated by a common ray tracing procedure, on several processors. An analytical model for the communication/ computation ratio is presented in order to predict the specific performance on different kind of architectures. A scalability analysis based on the isoefficiency function is performed on our parallel code when we increase the size of the problem and the number of processors. Tests are done to validate the analytical model by comparing the results to those given by the empirical tests. Results show that our parallel code is scalable and portable.
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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 | 0.002 | 0.010 |
| 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 | 0.002 |
| 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".