MétaCan
Menu
Back to cohort
Record W52060701

Characterizing the Sort Operation on Multithreaded Architectures.

2008· article· en· W52060701 on OpenAlexaff
Layali Rashid, Wessam Hassanein, Moustafa A. Hammad

Bibliographic record

VenueParallel and Distributed Processing Techniques and Applications · 2008
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
Fundersnot available
KeywordsMultithreadingComputer sciencesortParallel computingSimultaneous multithreadingParallelism (grammar)Computer architectureThread (computing)Operating system
DOInot available

Abstract

fetched live from OpenAlex

The Sort operation is a core part of many critical applications. Despite the large efforts to parallelize it, the fact that it suffers from high data-dependencies vastly limits its performance. Multithreaded architectures are emerging as the most demanding technology in leading-edge processors. These architectures include Simultaneous Multithreading, Chip Multiprocessors and machines combining different multithreading technologies. In this paper, we analyze the memory behavior and improve the performance of the most recent parallel radix and quick integer sort algorithms on modern multithreaded architectures. We achieve speedups up to 4.69x for radix sort and up to 4.17x for quick sort on a machine with 4 multithreaded processors compared to single threaded versions, respectively. We find that since radix sort is CPU-intensive, it exhibits better results on Chip multiprocessors where multiple CPUs are available. While quick sort is accomplishing speedups on all types of multithreading processers due to its ability to overlap memory miss latencies with other useful processing.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.267
Teacher spread0.245 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueParallel and Distributed Processing Techniques and ApplicationsSame topicParallel Computing and Optimization TechniquesFrench-language works237,207