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Record W2165075869 · doi:10.1109/iv.2006.48

Efficient Multicast Algorithms for Mesh-connected Multicomputers

2006· article· en· W2165075869 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
TopicInterconnection Networks and Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsMulticastComputer scienceProtocol Independent MulticastDistance Vector Multicast Routing ProtocolXcastComputer networkSource-specific multicastDistributed computingPragmatic General MulticastMulticast addressAlgorithmParallel computing

Abstract

fetched live from OpenAlex

Performance of multicomputers largely depends on that of the underlying network communications such as multicast. Two major parameters used to evaluate multicast routing are the time it takes to deliver the message to all destinations and the traffic which refers to the total number of links involved. Mesh is a network topology widely used in multicomputers. It has been proved that, in mesh network, it is NP-hard to find the multicast routing which is optimal on both time and traffic. In this paper, we proposed two efficient multicast algorithms designed for store-and-forward switched mesh-connected multicomputers: DIAG and DDS. They are both tree-based shortest path multicast algorithms whose complexity is O(KN) or less. Performance evaluations of these algorithms resulted from simulations are given at the end.

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: none
Teacher disagreement score0.874
Threshold uncertainty score0.524

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 science0.0000.000
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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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

Quick stats

Citations8
Published2006
Admission routes1
Has abstractyes

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