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Record W2764223492 · doi:10.1109/access.2017.2760251

A DAQM-Based Load Balancing Scheme for High Performance Computing Platforms

2017· article· en· W2764223492 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Access · 2017
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaHuawei Technologies
KeywordsComputer scienceScheme (mathematics)Load balancing (electrical power)Distributed computingQueueConvergence (economics)Queueing theorySupercomputerUtility maximizationQueue management systemStability (learning theory)Parallel computingComputer network

Abstract

fetched live from OpenAlex

This paper addresses the load balancing problem, which is one of the key issues in high-performance computing (HPC) platforms. A novel method, called decentralized active queue management (DAQM), is proposed to provide a fair task distribution in a heterogeneous computing environment for HPC platforms. An implementation of the DAQM is presented, which consists of an ON-OFF queue control and a utility maximization-based coordination scheme. The stability of the queue control scheme and the convergence of the algorithm for utility maximization have been assessed by rigorous analysis. To demonstrate the performance of the developed queueing control system, numerical simulations are carried out and the obtained results confirm the efficiency and viability of the developed scheme.

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 categoriesScholarly communication
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 score0.737
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0040.000
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 head0.044
GPT teacher head0.313
Teacher spread0.269 · 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