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Record W2996936519 · doi:10.2514/6.2020-1389

Application of ModelCenter to Real World Distributed and Parallel Execution Challenges

2020· article· en· W2996936519 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

VenueAIAA Scitech 2020 Forum · 2020
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsComputer scienceWorkflowDistributed computingContext (archaeology)Node (physics)AutomationLoad balancing (electrical power)Execution timeParallel computingDatabaseGrid

Abstract

fetched live from OpenAlex

Phoenix Integration worked with Lockheed Martin to apply ModelCenter software in a distributed and parallel computing context using ad hoc resources to the execution of MDO workflows for conceptual design of an aircraft. In the course of this work, the authors identified issues with automation of analysis tools in a parallel context, and developed methods to overcome those issues. The authors also performed a series of studies and to understand and characterize the performance of workflow execution using load balancing systems and ad hoc compute resources. Results of these studies indicate that while lighter workloads appear to scale well, more intensive workloads featuring heavy file I/O operations appear to be resource limited and prone to failure at higher levels of parallelization on each compute node, while scaling to higher number of compute nodes would be a more effective application of available resources.

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: none
Teacher disagreement score0.989
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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