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Record W2163816448 · doi:10.1109/aps.2009.5171722

Acceleration of large-scale FDTD simulations on high performance GPU clusters

2009· article· en· W2163816448 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

VenueDigest - IEEE Antennas and Propagation Society. International Symposium · 2009
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsAcceleware (Canada)
FundersNvidia
KeywordsAccelerationComputational scienceGraphics processing unitGPU clusterComputer scienceScalabilityFinite-difference time-domain methodCluster (spacecraft)CUDAParallel computingSoftwareComputer clusterGraphicsGeneral-purpose computing on graphics processing unitsScale (ratio)Computer graphics (images)PhysicsOperating systemOptics

Abstract

fetched live from OpenAlex

In this paper,a scalable graphics processing unit (GPU) cluster solution for the acceleration of FDTD for large-scale simulations is proposed. The hardware and software implementations are described. To illustrate the speed performance of the cluster, the simulation results of a cubic resonator with PEC boundaries is presented. A realistic large-scale simulation performed using SEMCAD X from SPEAG, accelerated by Acceleware's software libraries and GPU cluster solution is also presented.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.652

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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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