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Record W2579368542 · doi:10.14778/3025111.3025122

Persistent hybrid transactional memory for databases

2016· article· en· W2579368542 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the VLDB Endowment · 2016
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransactional memoryComputer scienceScalabilitySoftware transactional memorySynchronization (alternating current)ConcurrencyImplementationTransactional leadershipDatabase transactionConcurrency controlEmbedded systemOperating systemParallel computingDatabaseSoftware engineeringComputer network

Abstract

fetched live from OpenAlex

Processors with hardware support for transactional memory (HTM) are rapidly becoming commonplace, and processor manufacturers are currently working on implementing support for upcoming non-volatile memory (NVM) technologies. The combination of HTM and NVM promises to be a natural choice for in-memory database synchronization. However, limitations on the size of hardware transactions and the lack of progress guarantees by modern HTM implementations prevent some applications from obtaining the full benefit of hardware transactional memory. In this paper, we propose a persistent hybrid TM algorithm called PHyTM for systems that support NVM and HTM. PHyTM allows hardware assisted ACID transactions to execute concurrently with pure software transactions, which allows applications to gain the benefit of persistent HTM while simultaneously accommodating unbounded transactions (with a high degree of concurrency). Experimental simulations demonstrate that PHyTM is fast and scalable for realistic workloads.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.229

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 science0.0010.000
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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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