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Record W2092092276 · doi:10.13034/cysj-2014-011

Research Analyses of “Parellelization of the Knapsack Problem as an Introductory Experience in Parellel Computing”

2014· article· en· W2092092276 on OpenAlexaffvenueabout
Nikki Sigurdson

Bibliographic record

VenueJournal of Student Science and Technology · 2014
Typearticle
Languageen
FieldComputer Science
TopicComputability, Logic, AI Algorithms
Canadian institutionsEarl Haig Secondary School
Fundersnot available
KeywordsKnapsack problemComputer scienceLigneMathematics educationLibrary scienceHumanitiesMathematicsArtAlgorithm

Abstract

fetched live from OpenAlex

It may not take super powers to understand super computing, but the next three reports by students at Earl Haig Secondary School, analyze the super-computer research of a University of Mary Washington course. Using new perspectives from their own online computing course, the Earl Haig S.S. students wrote critical analyses of the Washington computer course conclusions. The students looked at the advantages and disadvantages of problem solving with computer codes on a single processor and dozens of processors. The problem to solve for the UoMW students was filling a virtual knapsack. No, you do not have super powers because you filled your own knapsack today! But it does take a super computer to figure out every possible way to fill it in a timely fashion. The students in Washington demonstrated techniques for code parallelization with Message Passing Interface and Open Multi Processing. Those coding techniques were initialized at UoMW and executed at the University of Texas, where the super-computer resides. Across Canada and international borders, the results of that research have inspired undergrads and secondary students alike. Here are the reac¬tions of three super thinkers from Earl Haig S.S. Peut-être on n’a pas besoin des super pou-voirs pour comprendre super-ordinateurs, parce que les trois prochains rapports d'étudiants de Earl Haig Secondary School, analyser la recherche d’un cours de super-ordinateur par l'Université de Mary Washington. En utilisation leur nouvelles perspectives de leur propre cours d'informatique en ligne, les élèves de SS Earl Haig ont écrit analyses critiques des conclusions sur le cours d'informatique Washington. Les étudiants se sont penchés sur les avan-tages et les inconvénients de résoudre les codes informatiques sur un seul processeur et des dizaines de processeurs. Le problème à résoudre pour les étudiants UoMW était de remplir un sac à dos virtuel . Il necessite un super-ordinateur pour comprendre tous les moyens possibles de le remplir en temps opportun. Les étudiants de Washington ont montré des techniques de parallélisation avec Message Passing Interface et Open Multi Processing. Ces techniques ont été initialisés à l’Université de Mary Washington et exécutés à l'Université du Texas, où le super-ordinateur réside. Partout au Canada et les frontières internationales, les résultats de cette recherche ont inspiré étudiants de premier cycle et élèves du secondaire de même. Voici les réactions des trois penseurs superbes de Earl Haig SS.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0060.008
Scholarly communication0.0080.009
Open science0.0020.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0090.002

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 head0.069
GPT teacher head0.421
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations0
Published2014
Admission routes3
Has abstractyes

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