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Record W2161873733 · doi:10.1109/tpwrs.2010.2042084

SIMD-Based Large-Scale Transient Stability Simulation on the Graphics Processing Unit

2010· article· en· W2161873733 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

VenueIEEE Transactions on Power Systems · 2010
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSIMDGraphics processing unitComputer scienceTransient (computer programming)GraphicsComputationParallel computingComputational scienceGeneral-purpose computing on graphics processing unitsSoftwareStability (learning theory)Central processing unitCUDAComputer hardwareComputer graphics (images)AlgorithmOperating system

Abstract

fetched live from OpenAlex

This paper presents a single-instruction-multiple-data (SIMD) based implementation of the transient stability simulation on the Graphics Processing Unit (GPU). Two programming models to implement the standard method of the transient stability simulation are proposed and implemented on a single GPU. In the first model the CPU is responsible for part of the simulation, while the onerous computations were offloaded to the GPU, creating a hybrid GPU-CPU simulator. In the second model, the GPU performs all the computations, while the CPU simply monitors the flow of the simulation. The accuracy of the proposed methods are validated using the PSS/E software for several large test systems. A substantial increase in speed was observed for the GPU-based simulations.

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)0.001
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: none
Teacher disagreement score0.912
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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