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Record W4235527144 · doi:10.1145/3155284.3019027

POSTER

2017· article· en· W4235527144 on OpenAlexaff
Arnamoy Bhattacharyya, Mike Dai Wang, Mihai Burcea, Yi Ding, Allen Deng, Sai Varikooty, Shafaaf Hossain, Cristiana Amza

Bibliographic record

VenueACM SIGPLAN Notices · 2017
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceSoftware transactional memoryTransactional memoryScalabilitySoftwareBenchmark (surveying)Parallel computingDatabase transactionOperating systemProgramming language

Abstract

fetched live from OpenAlex

In this work, we introduce and experimentally evaluate a new hybrid software-hardware Transactional Memory prototype based on Intel's Haswell TSX architecture. Our prototype extends the applicability of the existing hardware support for TM by interposing a hybrid fall-back layer before the sequential, big-lock fall-back path, used by standard TSX-supported solutions in order to guarantee progress. In our experimental evaluation we use SynQuake, a realistic game benchmark modeled after Quake. Our results show that our hybrid transactional system,which we call HythTM, is able to reduce the number of transactions that go to the sequential software layer, hence avoiding hardware transaction aborts and loss of parallelism. HythTM optimizes application throughput and scalability up to 5.05x, when compared to the hardware TM with sequential fall-back path.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.898

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 communication0.0010.001
Open science0.0030.001
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 head0.031
GPT teacher head0.285
Teacher spread0.254 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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