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Record W2278290577 · doi:10.4230/oasics.fsfma.2013.32

On the Determinism of Multi-core Processors

2013· preprint· en· W2278290577 on OpenAlexaff
Vladimir‐Alexandru Paun, Bruno Monsuez, Philippe Baufreton

Bibliographic record

VenueDROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2013
Typepreprint
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsCore (optical fiber)Computer scienceMulti-core processorDeterminismParallel computingPhilosophyTelecommunicationsEpistemology

Abstract

fetched live from OpenAlex

Hard real time systems are evolving in order to respond to the increasing demand in complex functionalities while taking advantage of newer hardware. Software development for safety critical systems has to comply with strict requirements that will facilitate the certification process. During this process, each part of the system is evaluated, requiring a certain level of assurance in order to provide confidence in the product. In particular there must be a level of confidence that the system behaves deterministically that may be based on functionality, resources and time. The success of system verification depends greatly on the capacity to determine its exact behavior. Nonetheless, hardware evolved in order to maximize the average computation power throughput with little to no regard to the deterministic aspect. Therefore modern architectural features of processors, like pipelines, cache memories and co-processors, make it hard to verify that all the needed properties are respected. The multi-core is furthermore difficult to analyze as the architecture employs mechanisms that compromise strong spatial and temporal partitioning when using shared resources without rigorous access control like shared caches or shared input/outputs. In this paper we identify and analyze the main sources of nondeterminism of the multi-cores with regard to the timing estimation. Precise determination of the worst case execution time is a challenging task even in single-core architectures. The problems are accentuated in the multi-core context mainly due to the resource sharing that can lead to highly complex interactions or to nondeterminism. Most of the units that generate behaviors that are hard to take into account can be deactivated, but it is not always easy to predict the impact on the performance. Nevertheless some of the features cannot be disabled (such as the out of order execution or some nondeterministic crossbar access policies) which leads to the invalidation of the respective platform for applications with high criticality level. We will address the problematic units, propose configuration or architecture guidelines and estimate their impact on the performance and determinism of the system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.294
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2013
Admission routes1
Has abstractyes

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