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Record W1981209094 · doi:10.1109/icpp.2013.81

Read-Write Lock Allocation in Software Transactional Memory

2013· article· en· W1981209094 on OpenAlexafffund
Amir Ghanbari Bavarsad, Ehsan Atoofian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSoftware transactional memoryTransactional memoryComputer scienceLock (firearm)ExploitDistributed computingOverhead (engineering)BottleneckConsistency modelParallel computingDatabase transactionData consistencyOperating systemEmbedded systemDatabaseComputer security

Abstract

fetched live from OpenAlex

Transactional Memory (TM) is a promising programming model for managing concurrent accesses to the shared memory locations. Time-based Software Transactional Memories (STMs) exploit a global clock to maintain consistency of transactions and validate transactional data. One of the shortcomings of this technique is that the global clock becomes bottleneck as the number of transactions increases. In this paper, we introduce two optimization techniques to overcome the overhead of the global clock. The first technique is Read-Write Lock Allocation (RWLA) which does not exploit any central data structure to maintain consistency of transactions. This method improves performance of STMs only if transactions commit successfully. However, in the event of frequent conflicts, RWLA increases cost of abort and degrades performance. Our second optimization technique is an adaptive technique which dynamically selects either baseline scheme or RWLA. Our experimental results reveal that our adaptive technique is effective and is able to improve performance of transactional applications up to 66%.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.546

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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 designOther design
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

Citations2
Published2013
Admission routes2
Has abstractyes

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