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Record W2031043969 · doi:10.1109/mm.2014.4

Cache Coherence for GPU Architectures

2014· article· en· W2031043969 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 Micro · 2014
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsAdvanced Micro Devices (Canada)University of British ColumbiaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Économie, de la Science et de l'Innovation - QuébecGame and Wildlife Conservation TrustNvidia
KeywordsComputer scienceParallel computingCache coherenceMulti-core processorCUDAComputer architectureJavaCoherence (philosophical gambling strategy)Programming paradigmGeneral-purpose computing on graphics processing unitsCacheCPU cacheOperating systemProgramming languageGraphicsCache algorithms

Abstract

fetched live from OpenAlex

GPUs have become an attractive target for accelerating parallel applications and delivering significant speedups and energy-efficiency gains over multicore CPUs. Programming GPUs, however, remains challenging because existing GPUs lack the well-defined memory model required to support high-level languages such as C++ and Java. The authors tackle this challenge with Temporal Coherence, a simple and intuitive timer-based coherence framework optimized for GPU.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.471
Threshold uncertainty score0.323

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 communication0.0000.000
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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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