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Record W2244331736 · doi:10.14778/2536222.2536255

Toward scalable transaction processing

2013· article· en· W2244331736 on OpenAlexaff
Anastasia Ailamaki, Ryan Johnson, Ippokratis Pandis, Pınar Tözün

Bibliographic record

VenueProceedings of the VLDB Endowment · 2013
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsScalabilityTransaction processing systemComputer scienceTransaction processingMulti-core processorOnline transaction processingDatabase transactionMetadataDistributed transactionDistributed computingComputer architectureEmbedded systemParallel computingDatabaseOperating system

Abstract

fetched live from OpenAlex

Designing scalable transaction processing systems on modern multicore hardware has been a challenge for almost a decade. The typical characteristics of transaction processing workloads lead to a high degree of unbounded communication on multicores for conventional system designs. In this tutorial, we initially present a systematic way of eliminating scalability bottlenecks of a transaction processing system, which is based on minimizing the unbounded communication. Then, we show several techniques that apply the presented methodology to minimize logging, locking, latching etc. related bottlenecks of transaction processing systems. In parallel, we demonstrate the internals of the Shore-MT storage manager and how they have evolved over the years in terms of scalability on multicore hardware through such techniques. We also teach how to use Shore-MT with the various design options it offers through its application layer Shore-Kits and Metadata Frontend.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.305

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 science0.0010.000
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 head0.015
GPT teacher head0.210
Teacher spread0.196 · 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 designBench or experimental
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

Citations7
Published2013
Admission routes1
Has abstractyes

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