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Record W2070314387 · doi:10.1145/2628071.2628099

What is the cost of weak determinism?

2014· article· en· W2070314387 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDeterminismComputer scienceThread (computing)Synchronization (alternating current)ParsecDistributed computingOverhead (engineering)SlowdownInterfacingReal-time computingOperating systemComputer hardwareComputer network

Abstract

fetched live from OpenAlex

We analyze the fundamental performance impact of enforcing a fixed order of synchronization operations to achieve weak deterministic execution. Our analysis is in three parts, performed on a real system using the SPLASH-2 and PARSEC benchmarks. First, we quantify the impact of various sources of non-determinism on execution of data-race-free programs. We find that thread synchronization is the prevalent source of non-determinism, sometimes affecting program output. Second, we divorce the implementation overhead of a system imposing a specific synchronization order from the impact of enforcing this order. We show that this fundamental cost of determinism is small (slowdown of 4% on average and 32% in the worst case) and we identify application characteristics responsible for this cost. Finally, we evaluate this cost under perturbed execution conditions. We find that demanding determinism when threads face such conditions can cause almost 2x slowdown.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.131

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 head0.017
GPT teacher head0.268
Teacher spread0.251 · 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