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Record W4285451316 · doi:10.32920/ryerson.14662569

High performance computing for linear acoustic wave simulation

2021· preprint· en· W4285451316 on OpenAlexfundno aff
Fouad Butt

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsComputer scienceComputationParallel computingImplementationComputational scienceAcoustic wave equationAcoustic waveAcoustic modelSPMDReduction (mathematics)Computer engineeringAlgorithmAcousticsMathematicsPhysicsSpeech recognition

Abstract

fetched live from OpenAlex

Parallel computing techniques are applied to a linear acoustic wave model to reduce execution time. Three parallel computing models are developed to parallelize computations. The fork-and-join, SPMD and SIMT models define the execution of parallel computations. The precision and efficiency of the linear acoustic wave model are improved through substantial speedups in all implementations. Furthermore, axisymmetric properties of certain acoustic fields lead to a reduction in the spatio-temporal complexity of those acoustic fields by removing redundant computations. The same linear acoustic wave model is also modified and extended to describe wave propagation across multiple media instead of only a single medium. The developed implementations are integrated into a particularly useful package for high performance simulation of two- or three-dimensional linear acoustic elds generated by realistic sources in various fluid media.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.487
Threshold uncertainty score0.999

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)0.0020.000

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 head0.063
GPT teacher head0.295
Teacher spread0.232 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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