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Record W4396681647 · doi:10.1051/epjconf/202429501046

Scale tests of the new DUNE data pipeline

2024· article· en· W4396681647 on OpenAlexfundno aff
S. Timm

Bibliographic record

VenueEPJ Web of Conferences · 2024
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsnot available
FundersHigh Energy PhysicsInstitut National de Physique Nucléaire et de Physique des ParticulesHorizon 2020 Framework ProgrammeScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaOffice of ScienceEuropean CommissionMinisterio de Ciencia e InnovaciónFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroConselho Nacional de Desenvolvimento Científico e TecnológicoCentre National de la Recherche ScientifiqueEuropean Regional Development FundMinisterstvo Školství, Mládeže a TělovýchovyU.S. Department of EnergyJunta de AndalucíaFundação para a Ciência e a TecnologiaFundação de Amparo à Pesquisa do Estado de GoiásFermilabUK Research and InnovationNational Science FoundationRoyal SocietyNational Energy Research Scientific Computing CenterXunta de GaliciaCERNFundação de Amparo à Pesquisa do Estado de São PauloTürkiye Bilimsel ve Teknolojik Araştırma KurumuSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsDaemonLarge Hadron ColliderPipeline (software)PetabyteMetadataData acquisitionComputer scienceDetectorOperating systemPhysicsParticle physicsBig dataTelecommunications

Abstract

fetched live from OpenAlex

In preparation for the second runs of the ProtoDUNE detectors at CERN (NP02 and NP04)[1], DUNE has established a new data pipeline for bringing the data from the EHN-1 experimental hall at CERN to primary tape storage at Fermilab and CERN, and then spreading it out to a distributed disk data store at many locations around the world. This system includes a new Ingest Daemon and a new Declaration Daemon. The Rucio[2] replica catalog, and FTS3 transport are used to transport all files. All file metadata is declared to the new MetaCat[3] metadata service. All of these new components have been successfully tested at a scale equal to the expected output of the detector data acquisition system (~2-4 GB/s), and the expected network bandwidth out of the experimental hall. We present the procedure that was used to test and the results of the test.

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)0.008
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.006
Open science0.0030.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.045
GPT teacher head0.302
Teacher spread0.257 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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