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Two-Core Parallel Network Solution for NovaCor<sup>®</sup> Real-Time Digital Simulator

2022· article· en· W4313040594 on OpenAlexaff
Yizhong Hu, Yi Zhang, Trevor Maguire

Bibliographic record

Venue2022 IEEE Power &amp; Energy Society General Meeting (PESGM) · 2022
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsRTDS Technologies (Canada)
Fundersnot available
KeywordsCholesky decompositionComputer scienceParallel computingNode (physics)DecompositionComputationChassisTransient (computer programming)SimulationAlgorithmEngineeringOperating system

Abstract

fetched live from OpenAlex

Multi-processor parallel computation can be applied to the network solution of electromagnetic transient simulation, to improve computational performance. RTDS® has implemented this feature on its NovaCor <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> simulator, making it capable to solve a maximum 600 nodes network on each chassis. This paper first reviews traditional network solution including Cholesky decomposition, forward/backward operations, and node number ordering. Then the practice of implementing two-core parallel network solution is reported. This is mainly achieved by group bisection and coordinating two cores appropriately to fulfill the decomposition as well as the forward and backward operations together. Demonstration cases are given in the end of the paper.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
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 score0.217
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.234
Teacher spread0.220 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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