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Record W209015621 · doi:10.1504/ijhpcn.2006.013484

MPI scalability of a large memory LES code

2006· article· en· W209015621 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

VenueInternational Journal of High Performance Computing and Networking · 2006
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsQueen's University
Fundersnot available
KeywordsScalabilityParallel computingTurbulenceComputer scienceComputational scienceCode (set theory)Finite volume methodGridLarge eddy simulationReynolds numberJet (fluid)Flow (mathematics)VisualizationDetached eddy simulationMechanicsReynolds-averaged Navier–Stokes equationsPhysicsOperating systemGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract — Large eddy simulations (LES) of co-flowing round jets with 33.6 million grid points are carried out using 16 Sun 1 GHz UltraSPARC III Cu processors. An in-depth processor scalability analysis is carried out for an MPI based code for a finite-volume solution of time dependent Navier-Stokes equations. The solver is based on a second-order structured staggered grid discretization, second-order time advancement, and a multi-grid Poisson equation for pressure. Particular attention is paid to the effect of initial conditions on the spatial development of the co-flowing jet at a Reynolds number of 7,300. A co-flow velocity to initial jet centreline velocity ratio of 1:11 and a coflow to initial jet diameter ratio of 35:1 are used to match the flow cases of [12]. The ¢¡¤£¦¥¨§ © �¡�£ simulation volume, where £ is the orifice diameter at the jet inlet, is divided into

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 score0.103
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.217
Teacher spread0.212 · 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