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Record W2138301507 · doi:10.1109/pads.2011.5936760

Parallel Discrete Event N-Body Dynamics

2011· article· en· W2138301507 on OpenAlexaff
Matthew Holly, Carl Tropper

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceConcurrencyDiscrete event simulationEvent (particle physics)ScalingParallel computingConcurrent computingComputationTheoretical computer scienceAlgorithmComputational scienceDistributed computingMathematicsSimulation

Abstract

fetched live from OpenAlex

Numerical simulation of gravitational N-body systems is an important tool for studying the dynamic behaviour of stellar systems, and in some cases is the only option available given the extremely large time scales involved. The direct summation approach, which evaluates the force between each pair of particles at each time step, produces the most accurate results. However despite many algorithmic advances this method remains a computationally challenging problem owing to its O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) scaling characteristics. The desire to model increasingly larger systems has spurred the adoption of parallel computation techniques, but unfortunately many of the strategies used to accelerate sequential direct N-body simulations hinder their efficient parallelization. This paper investigates the use of parallel discrete event simulation as an alternative to the usual iterative time-stepping approach. By decomposing typical operations into finer-grained events, it is shown that there exists considerable potential for exploiting the model's inherent concurrency. In addition, it is demonstrated how certain optimizations that are normally difficult to parallelize are incorporated naturally into the parallel discrete event paradigm.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score1.000

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)0.0040.001

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 head0.146
GPT teacher head0.423
Teacher spread0.278 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations1
Published2011
Admission routes1
Has abstractyes

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