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Record W2948863035 · doi:10.1109/access.2019.2920917

Parallel Multidimensional Lookahead Sorting Algorithm

2019· article· en· W2948863035 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

VenueIEEE Access · 2019
Typearticle
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceSpeedupParallel computingOverhead (engineering)SortingSorting algorithmAlgorithmLocalityLocality of referenceParallel algorithmCPU cacheCache-oblivious algorithmCacheCache algorithms

Abstract

fetched live from OpenAlex

This paper presents a new parallel structured lookahead multidimensional sorting algorithm. Our algorithm can be based on any sequential sorting algorithm. The amount of parallelism can be controlled using several parameters such as the number of threads, word size, memory/processor communication overhead, and the dimension of the algorithm. The proposed technique is ideally suited for general purpose graphic processing units and shared-memory massively parallel processor systems. It ensures that data being processed exhibits temporal and spatial locality to maximize the utilization of processor cache. The algorithm achieves a speedup even when a single processor is used. A lookahead algorithm is also proposed to achieve even higher speedup. The performance of the proposed algorithm is verified numerically and experimentally.

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 score0.967
Threshold uncertainty score0.824

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 communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.297
Teacher spread0.272 · 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