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Record W2118089750 · doi:10.1145/1013329.1013337

Event reconstruction in time warp

2004· article· en· W2118089750 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 - Workshop on Parallel and Distributed Simulation · 2004
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceOverhead (engineering)Event (particle physics)GranularityQueueParallel computingState (computer science)Discrete event simulationReal-time computingDistributed computingEmbedded systemAlgorithmOperating systemSimulationComputer network

Abstract

fetched live from OpenAlex

It optimistic simulations, checkpointing techniques are often used to reduce the overhead caused by state saving. In this paper, we propose event reconstruction as a technique with which to reduce the overhead caused by event saving, and compare its memory consumption and execution time to the results obtained by dynamic checkpointing. As the name implies, event reconstruction reconstructs in put events and anti-events from the differences between adjacent states, and does not save input events in the event queue.For simulations with fine event granularity and small state size, such as the logic simulation of VLSI circuitry, event reconstruction can yield an improvement in execution time as well as a significant reduction in memory utilization when compared to dynamic checkpointing. Moreover, this technique facilitates load migration because only the state queue needs to be moved from one processor to another.

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)0.001
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.059
GPT teacher head0.369
Teacher spread0.311 · 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