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Record W2170078808 · doi:10.1109/ipdps.2008.4536466

A parallel sewing method for solving tridiagonal Toeplitz strictly diagonally dominant systems

2008· article· en· W2170078808 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

VenueProceedings - IEEE International Parallel and Distributed Processing Symposium · 2008
Typearticle
Languageen
FieldComputer Science
TopicMatrix Theory and Algorithms
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsToeplitz matrixTridiagonal matrixDiagonalComputer scienceDiagonally dominant matrixCoefficient matrixParallel computingLinear systemBand matrixMatrix (chemical analysis)Scale (ratio)AlgorithmMathematicsSymmetric matrixSquare matrixMathematical analysisPure mathematicsGeometry

Abstract

fetched live from OpenAlex

The large scale of linear systems of equations results in costly solving time. These systems usually have specific properties that can be used for designing fast algorithms. In addition, using parallel programming on distributed memory clusters enables us to get the results even faster. This work introduces a new fast parallel algorithm for solving systems with a strictly diagonally dominant three-band Toeplitz coefficient matrix. We call this new method the sewing method because the boundaries sew the adjacent subsystems together.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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