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Record W1992053203 · doi:10.1109/pdcat.2012.132

TRT: Transactional Read Tracking

2012· article· en· W1992053203 on OpenAlexaff
Amir Ghanbari Bavarsad, Ehsan Atoofian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsLakehead University
Fundersnot available
KeywordsTransactional memorySoftware transactional memoryComputer scienceExploitConsistency (knowledge bases)CommitTransactional leadershipOverhead (engineering)Transaction processingDistributed computingConsistency modelDatabase transactionParallel computingData consistencyOperating systemDatabaseComputer securityArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Many recent Software Transactional Memory (STM) algorithms exploit a global clock to maintain consistency among transactions. While this method is simple to implement it results in contention over the global clock, especially when transactions commit frequently. In this paper, we introduce Transactional Read Tracking (TRT) which does not exploit any central data structure to maintain consistency of transactions. TRT tracks transactional read and write operations and aborts transactions if they conflict over a shared memory location. As such, TRT maintains consistency and eliminates the overhead of the global clock. Our experimental results reveal that this method is effective and is able to improve performance of transactional applications up to 63%.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.246

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 communication0.0000.001
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 head0.024
GPT teacher head0.253
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

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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2012
Admission routes1
Has abstractyes

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