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Record W3098178495 · doi:10.1051/epjconf/202024507034

External Resources: Clouds and HPCs for the expansion of the ATLAS production system at the Tokyo regional analysis center

2020· article· en· W3098178495 on OpenAlexaff
Michiru Kaneda

Bibliographic record

VenueEPJ Web of Conferences · 2020
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsInstitute of Particle Physics
FundersScience and Technology Facilities Council
KeywordsLarge Hadron ColliderCloud computingAtlas (anatomy)Data centerCenter (category theory)GridEconomic shortageComputer scienceOperating systemDatabasePhysicsParticle physicsGeographyGeology

Abstract

fetched live from OpenAlex

The Tokyo regional analysis center at the International Center for Elementary Particle Physics, the University of Tokyo, is one of the Tier 2 sites for the ATLAS experiment in the Worldwide LHC Computing Grid. The current system has 10,752 CPU cores and 16 PB disk storage. CERN plans the HighLuminosity LHC starting from 2026, which increases the peak luminosity by 5 times compared to the present value at the LHC. A shortage of computing resources for the high-luminosity LHC is expected with the current system. Therefore new ideas to expand the system are necessary. To expand the ATLAS production system at the Tokyo regional analysis center, R&D using external resources has been launched. One kind of external resources is a commercial cloud resource, such as Google Cloud Platform. Another resource is the HighPerformance Computer (HPC) at the University of Tokyo. In this contribution, the current status of the R&D, the systems for these resources, and comparisons of the cost are reported.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.035
GPT teacher head0.241
Teacher spread0.206 · 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 designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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