A new petabyte-scale data derivation framework for ATLAS

21st International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Catmore, James^1 ; Cranshaw, Jack^2 ; Gillam, Thomas^3 ; Gramstad, Eirik^1 ; Laycock, Paul^4 ; Ozturk, Nurcan^5 ; Stewart, Graeme Andrew^6

Fysikkbygningen, University of Oslo, P.O. Box 1048 Blindern, Oslo

N-0316, Norway^1

Argonne National Laboratory, 9700 S. Cass Avenue, Argonne

IL

60439, United States^2

Cavendish Laboratory, 19 J J Thomson Avenue, Cambridge

CB3 0HE, United Kingdom^3

University of Liverpool, Oliver Lodge Laboratory, Liverpool

L69 7ZE, United Kingdom^4

University of Texas at Arlington, Box-19059, Arlington

TX

76019, United States^5

School of Physics and Astronomy, University of Glasgow, Glasgow

G12 8QQ, United Kingdom^6

关键词: Analysis models;    Computing resource;    Domain specific languages;    Event selection;    Logical operations;    Physics analysis;    Reconstruction software;    Tool requirements;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/664/7/072007/pdf
DOI  :  10.1088/1742-6596/664/7/072007

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

During the Long Shutdown of the LHC, the ATLAS collaboration overhauled its analysis model based on experience gained during Run 1. A significant component of the model is a "Derivation Framework" that takes the petabyte-scale AOD output from ATLAS reconstruction and produces samples, typically terabytes in size, targeted at specific analyses. The framework incorporates all of the functionality of the core reconstruction software, while producing outputs that are simply configured. Event selections are specified via a concise domain-specific language, including support for logical operations. The output content can be highly optimised to minimise disk requirements, while maintaining the same C++ interface. The framework includes an interface to the late-stage physics analysis tools, ensuring that the final outputs are consistent with tool requirements. Finally, the framework allows several outputs to be produced for the same input, providing the possibility to optimise configurations to computing resources.