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Record W1997758669 · doi:10.5555/2485288.2485384

Using synchronization stalls in power-aware accelerators

2013· article· en· W1997758669 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

VenueDesign, Automation, and Test in Europe · 2013
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRegister filePower gatingComputer scienceIdlesyncSynchronization (alternating current)Leakage (economics)Embedded systemEfficient energy usePower (physics)Operating systemComputer hardwareParallel computingComputer networkInstruction setEngineeringElectrical engineeringVoltageChannel (broadcasting)

Abstract

fetched live from OpenAlex

GPUs spend significant time on synchronization stalls. Such stalls provide ample opportunity to save leakage energy in GPU structures left idle during such periods. In this paper we focus on the register file structure of NVIDIA GPUs and introduce sync-aware low leakage solutions to reduce power. Accordingly, we show that applying the power gating technique to the register file during synchronization stalls can improve power efficiency without considerable performance loss. To this end, we equip the register file with two leakage power saving modes with different levels of power saving and wakeup latencies.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.027
GPT teacher head0.257
Teacher spread0.230 · 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