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Record W2276747962 · doi:10.1007/0-306-47015-2_26

A Programming System for Parallel Execution of Fortran Subprograms in Distributed Environment

2005· book-chapter· en· W2276747962 on OpenAlexaff
Khaled M. Ben Hamed, Weichang Du

Bibliographic record

VenueKluwer Academic Publishers eBooks · 2005
Typebook-chapter
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsComputer scienceFortranParallel computingProgramming languageObject-oriented programmingScheduling (production processes)Programming paradigmOperating system

Abstract

fetched live from OpenAlex

This paper presents the design and implementation of a high-level parallel programming system to support parallel execution of Fortran subprograms. The objective of the system is to unburden users from the responsibility of programming details including discovering and managing parallelism such as partitioning, mapping, scheduling, and load balancing, hence allowing users to concentrate on problem solving. From the language perspective, the system integrates the reuse of legacy Fortran code with the intensional programming paradigm. At runtime, coarse-grain parallelism can be exploited to allow concurrent execution of Fortran subprograms on sequential machines connected by a local or wide area network. From the implementation perspective, an application program written in this system is mapped onto an object-oriented parallel generator/executer abstract program architecture. At runtime, an object-oriented computing engine is generated from the architecture to coordinate the parallel executions based on the intensional semantics.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.005

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 head0.021
GPT teacher head0.241
Teacher spread0.219 · 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
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

Citations0
Published2005
Admission routes1
Has abstractyes

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