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Record W2117895458 · doi:10.1109/pcee.2000.873593

C++2MPI: a software tool for automatically generating MPI datatypes from C++ classes

2002· article· en· W2117895458 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversité du QuébecUniversité du Québec en Outaouais
Fundersnot available
KeywordsComputer sciencePortingProgramming languageMessage passingCall graphGraphSoftwareMessage Passing InterfaceClass (philosophy)Set (abstract data type)Interface (matter)Theoretical computer scienceParallel computingArtificial intelligence

Abstract

fetched live from OpenAlex

The Message Passing Interface I.I (MPI I.I) standard defines a library of message-passing functions for parallel and distributed computing. We have developed a new software tool called C++2MPI which can automatically generate MPI derived datatypes for a specified C++ class. C++2MPI can generate data types for derived classes, for partially and fully-specialized templated classes, and for classes with private data members. Given one or more user-provided classes as input, C++2MPI generates, compiles and archives a function for creating the MPI derived datatype. When the generated function is executed, it builds the derived MPI datatype if the datatype does not already exist, and returns the value of an MPI handle for referencing the datatype. PGMT (Processing Graph Method Tool) is a set of application program interfaces for porting the Processing Graph Method (PGM), a parallel programming method, to diverse networks of processors. C++2MPI was developed as a component of PGMT, but can be used as a stand-alone tool.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.971
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.267
Teacher spread0.234 · 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