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Record W2152350268 · doi:10.1109/ecbs.1995.521874

A protection cache architecture for the multi-view memory model and its performance

2002· article· en· W2152350268 on OpenAlexaff
Peter Bodorik, Dawn Jutla, A. Davis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsTechnical University of Nova Scotia
Fundersnot available
KeywordsComputer scienceCacheCache-only memory architectureFlexibility (engineering)ArchitectureCPU cacheAccess controlData accessCache pollutionProtocol (science)Cache coloringCache algorithmsComputer architectureDistributed computingOperating systemDatabase

Abstract

fetched live from OpenAlex

This paper presents a supporting architecture for the multi-view memory model and investigates its performance. The multi-view memory model is intended to provide applications with the variable-sized access units that they require for optimal performance within a computer system. The supporting architecture provides for the addressability of the state information which is kept on each variable sized-access unit. It is also designed to provide applications with a choice of access control protocol on various memory regions. Caches are integral to the architectural design. They allow for on-the-fly access rights determination and also for the flexibility of protocol choices. The miss rates on the caches within the access control protection subsystem architecture are determined using synthetic traces as input. The emphasis in this study is not to determine how traditional factors such as cache size, degree of associativity and line size affect the miss rates, but is targeted at evaluating the effect that views and their definitions have on the performance. It is found that the protection cache miss rate vary according to the sites of the access units defined within the views and the number and ratio of the differently-sized access unit entries within the protection cache.

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)0.000
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 score0.523
Threshold uncertainty score0.202

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 science0.0000.000
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 head0.060
GPT teacher head0.258
Teacher spread0.198 · 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
Published2002
Admission routes1
Has abstractyes

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