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Record W1544601224 · doi:10.1109/ipps.1996.508061

Dome: parallel programming in a distributed computing environment

2002· article· en· W1544601224 on OpenAlexaff
José Nagib Cotrim Árabe, Adam Beguelin, Bruce Lowekamp, Erik Seligman, M. Starkey, Peter Stephan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsComputer scienceDistributed objectDistributed computingLoad balancing (electrical power)Synchronization (alternating current)Dome (geology)ProgrammerArchitectureObject (grammar)Parallel computingOperating systemGridCommon Object Request Broker ArchitectureComputer networkGeology

Abstract

fetched live from OpenAlex

The Distributed object migration environment (Dome) addresses three major issues of distributed parallel programming: ease of use, load balancing, and fault tolerance. Dome provides process control, data distribution, communication, and synchronization for Dome programs running in a heterogeneous distributed computing environment. The parallel programmer writes a C++ program using Dome objects which are automatically partitioned and distributed over a network of computers. Dome incorporates a load balancing facility that automatically adjusts the mapping of objects to machines at runtime, exhibiting significant performance gains over standard message passing programs executing in an imbalanced system. Dome also provides checkpointing of program state in an architecture independent manner allowing Dome programs to be checkpointed on one architecture and restarted on another.

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)0.003
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0030.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0110.009

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 head0.220
Teacher spread0.199 · 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

Citations30
Published2002
Admission routes1
Has abstractyes

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