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Record W2012031157 · doi:10.1109/mcsoc.2015.42

Lighting the Dark-Silicon 3D Chip Multi-processors by Exploiting Heterogeneity in Cache Hierarchy

2015· article· en· W2012031157 on OpenAlexaff
Ashkan Sadeghi, Kaamran Raahemifar, Mahmood Fathy, Arghavan Asad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceExploitMemory hierarchyCacheThread (computing)Multi-core processorParallel computingComputer architectureEmbedded systemMultithreadingMicroarchitectureCPU cacheChipInstruction setMemory architectureOperating system

Abstract

fetched live from OpenAlex

This paper addresses a set of design paradigms by exploiting device and architectural heterogeneity to mitigate the dark silicon. We exploit Non-Volatile Memory (NVM) as potential replacements to conventional caches. Also, we study the problem of dynamic thread mapping in future Chip Multi-Processors (CMPs) via an efficient scheduler. Evaluations on a 3D architecture consisting of 8 core (a big and a small core on each tile) show that the proposed method provides up to 7% average performance improvement for multithreaded benchmarks, and 9% for multiprogrammed workloads. The results also show 62.5% and 67.7% on average energy-delay product (EDP) improvement for multithreaded and multiprogrammed workloads respectively, with 7.87% area overhead compared to the conventional methods.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.706
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.057
GPT teacher head0.295
Teacher spread0.238 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2015
Admission routes1
Has abstractyes

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