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Record W3194069769 · doi:10.1145/3476886.3477514

C2J

2021· article· en· W3194069769 on OpenAlexaff
Kyoungho Koo, Joontaek Oh, Kwangwon Min, Youngjin Kwon, Youjip Won

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsComputer scienceWorkloadJournaling file systemLatency (audio)Thread (computing)Operating systemDatabase transactionTransaction processingDatabaseComputer file

Abstract

fetched live from OpenAlex

In this work, we developed a new technique called Compulsory Compound Journaling (C2J) to increase the coalescing degree of shared updates in journal transactions. C2J introduces intentional delay in waking up the JBD thread to extend the interval during which a transaction has more chance to collect modified blocks. C2J dynamically determines the timing of journal transaction delay by accounting for the workload intensity and the speed of the storage device. We implemented C2J in EXT4. In our evaluation, we confirmed that C2J is effective under various workloads with various storage devices, improving the throughput up to 65\% in the mdtest and reducing the fsync() average latency and tail latency (99.99\%) up to 32\% and 36\%, respectively under varmail workload with 32 threads.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.007

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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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