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Record W2073600756 · doi:10.1145/2423636.2423642

Thermal-aware task scheduling in 3D chip multiprocessor with real-time constrained workloads

2013· article· en· W2073600756 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

VenueACM Transactions on Embedded Computing Systems · 2013
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsComputer scienceMultiprocessingScheduling (production processes)Parallel computingComputationChipDynamic priority schedulingFair-share schedulingDistributed computingArchitectureMultiprocessor schedulingEmbedded systemTwo-level schedulingReal-time computingAlgorithmComputer networkQuality of service

Abstract

fetched live from OpenAlex

Chip multiprocessor (CMP) techniques have been implemented in embedded systems due to tremendous computation requirements. Three-dimension (3D) CMP architecture has been studied recently for integrating more functionalities and providing higher performance. The high temperature on chip is a critical issue for the 3D architecture. In this article, we propose an online thermal prediction model for 3D chips. Using this model, we propose novel task scheduling algorithms based on rotation scheduling to reduce the peak temperature on chip. We consider data dependencies, especially inter-iteration dependencies that are not well considered in most of the current thermal-aware task scheduling algorithms. Our simulation results show that our algorithms can efficiently reduce the peak temperature up to 8.1 ˆ C.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.635
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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