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Record W2168193772 · doi:10.1109/icpp.2008.34

The MAP3S Static-and-Regular Mesh Simulation and Wavefront Parallel-Programming Patterns

2008· article· en· W2168193772 on OpenAlexaff
Robert Niewiadomski, José Nelson Amaral, Duane Szafron

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceParallel computingPolygon meshScripting languageDistributed memoryAsynchronous communicationComputationMemory footprintProgramming paradigmDistributed computingProgramming languageShared memoryComputer graphics (images)

Abstract

fetched live from OpenAlex

This paper presents the simulation and wavefront parallel-programming patterns of the MAP3S pattern-based parallel programming system for distributed-memory environments. Both patterns target iterative computations on static-and-regular meshes. In addition to providing performance-oriented features, such as asynchronous communication and distribution of the computational workload that is tailored to fit the computation, the patterns also provide usability-oriented features, such as direct mesh-access, mesh memory-footprint distribution, and a versatile data-dependency specification scripting-language. Parallel programs developed using MAP3S achieve significant performance gains and capability enhancements on both low-end and high-end interconnect-equipped distributed-memory systems.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.026
GPT teacher head0.265
Teacher spread0.239 · 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 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

Citations0
Published2008
Admission routes1
Has abstractyes

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