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Record W1887859263 · doi:10.1109/dftvs.1992.224357

Nondeterministic adaptive routing techniques for WSI processor arrays

2003· article· en· W1887859263 on OpenAlexaff
D.C. Blight, R.D. McLeod

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsNondeterministic algorithmComputer scienceHypercubeAdaptive routingRouting (electronic design automation)Distance-vector routing protocolParallel computingEqual-cost multi-path routingNetwork packetDistributed computingStatic routingRouting algorithmNetwork topologyMultipath routingDestination-Sequenced Distance Vector routingDynamic Source RoutingAlgorithmComputer networkRouting protocol

Abstract

fetched live from OpenAlex

Presents new adaptive routing algorithms for faulty processor arrays. Past research has shown that packet switched based communication performance in mesh connected networks is significantly degraded by the presence of faulty processors. Nondeterministic routing algorithms have been developed based on transport modeling of packet flow in disordered arrays. By utilizing nondeterministic routing strategies, based on biased random walkers, one can implement deadlock free routing, at the expense of not following the shortest path. These algorithms will be shown to be capable of increasing network bandwidth in the presence of faulty processors and interconnects. These algorithms offer an alternative to conventional adaptive routing techniques by utilizing a computationally simple algorithm based on local (nearest neighbor) information. Although the authors concentrate efforts on 2-dimensional processor arrays, the algorithms are also suitable for higher dimensional topologies such as hypercubes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.337

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 head0.028
GPT teacher head0.263
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2003
Admission routes1
Has abstractyes

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