Implementation of generalized coarse-mesh rebalance in NEWTRNX for acceleration of parallel block-jacobi transport
Bibliographic record
Abstract
The NEWTRNX [1] transport module solves the multigroup, discrete-ordinates source-driven or k-eigenvalue transport equation in parallel on a 3-D unstructured tetrahedral mesh using the extended step characteristics (ESC) [2], also known as the slice-balance approach (SBA), spatial discretization. The spatial domains are decomposed using METIS [3]. NEWTRNX is under development for nuclear reactor analysis on computer hardware ranging from clusters to massively parallel machines, like the Cray XT4. Transport methods that rely on full sweeps across the spatial domain have been shown to display poor scaling for thousands of processors. The Parallel Block-Jacobi (PBJ) algorithm allows each spatial partition to sweep over all discrete-ordinate directions and energies independently of all other domains, potentially allowing for much better scaling than possible with full sweeps [4]. The PBJ algorithm has been implemented in NEWTRNX using a Gauss-Seidel iteration in energy and an asynchronous communication by an energy group, such that each partition utilizes the latest boundary solution available for each group before solving the withingroup scattering in a given group. For each energy group, the within-group scattering converges with a generalized minimum residual (GMRES) solver [5], preconditioned with beta transport synthetic acceleration (β-TSA) [6].
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".