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Record W275975031 · doi:10.13182/nse150-155

Parallel Solver Based on the Three-Dimensional Characteristics Method: Design and Performance Analysis

2005· article· en· W275975031 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNuclear Science and Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSolverComputer scienceNeutron transportAccelerationComputational scienceParallel computingRay tracing (physics)SoftwareTracingFrame (networking)Applied mathematicsMathematical optimizationNeutronAlgorithmMathematicsPhysics

Abstract

fetched live from OpenAlex

AbstractRecent advances in parallel software development for solving three-dimensional (3-D) neutron transport problems using the characteristics method are presented. The characteristics method solves the transport equation by collecting local angular fluxes along neutron paths. In order to be able to solve large 3-D transport problems in a reasonable time frame, the characteristics solver needs to be accelerated. After applying adequate numerical acceleration techniques, the only issue is to parallelize the solver. The parallelization of this solver is based on distributing a group of tracks, generated by a ray-tracing procedure, on several processors. Different distributing schemes and load-balancing techniques based on a calculation load model are presented. A message-passing model is used to communicate the local solutions between processes participating in solving a problem. Both analytical models of this parallel algorithm and performance analysis are presented and illustrated by several examples.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.195
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it