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Record W1605442146 · doi:10.1109/pacrim.2003.1235873

Evolution and research applications of an objected-oriented framework for architectural simulation

2004· article· en· W1605442146 on OpenAlexafffund
Naraig Manjikian, Ngeow Wei Cheong, Yolanda Chong, Anthony Chow, Peter M. Ewert, X. Li, P.R. McHardy, L. Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsIBM (Canada)McGill UniversityQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceObject-oriented programmingVisualizationTracingMultiprocessingFront and back endsNetwork simulationArchitectureComputer architectureDistributed computingSimulationOperating systemData mining

Abstract

fetched live from OpenAlex

This paper describes the results of a long-term effort to develop and enhance a simulation framework called Quasar in support of research in computer architecture and its applications. Quasar is an object-oriented simulation tool that incorporates a front-end simulator for tracing instructions and a back-end simulator for modeling the interactions of system components through message-passing between objects. Particular attention has been given to simulation of multiprocessor systems with this tool. Over several years, simulation efficiency and utility have been unproved with numerous enhancements, including run-time configuration of simulations from specification files, tighter coupling of the front and back ends for increased execution efficiency, and dynamic graphical visualization of simulated performance metrics. The framework has proven its value through its use by several students to pursue graduate research on computing architectures for networking and stream-oriented applications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.497
Threshold uncertainty score0.201

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 head0.031
GPT teacher head0.369
Teacher spread0.337 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations2
Published2004
Admission routes2
Has abstractyes

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