CERN Accelerating science

Published Articles
Title Enabling Data Intensive Science on Supercomputers for High Energy Physics R&D; Projects in HL-LHC Era

Klimentov, Alexei (Brookhaven) ; Benjamin, Douglas (Argonne (main)) ; Di Girolamo, Alessandro (CERN) ; De, Kaushik (Texas U., Arlington) ; Elmsheuser, Johannes (Brookhaven) ; Filipcic, Andrej (Stefan Inst., Ljubljana) ; Kiryanov, Andrey (St. Petersburg, INP ; Moscow State U.) ; Oleynik, Danila (Dubna, JINR) ; Wells, Jack C (Oak Ridge) ; Zarochentsev, Andrey (St. Petersburg State U. ; Moscow State U.) ; Zhao, Xin (Brookhaven)

Imprint 8 p.
In: EPJ Web Conf. 226 (2020) 01007
In: Conference on Mathematical Modeling and Computational Physics, Stará Lesná, Slovakia, 1 - 5 July 2019, pp.01007
DOI 10.1051/epjconf/202022601007
Subject category Computing and Computers ; Particle Physics - Experiment
Accelerator/Facility, Experiment CERN HL LHC
Abstract The ATLAS experiment at CERN’s Large Hadron Collider uses the Worldwide LHC Computing Grid, the WLCG, for its distributed computing infrastructure. Through the workload management system PanDA and the distributed data management system Rucio, ATLAS provides seamless access to hundreds of WLCG grid and cloud based resources that are distributed worldwide, to thousands of physicists. PanDA annually processes more than an exabyte of data using an average of 350,000 distributed batch slots, to enable hundreds of new scientific results from ATLAS. However, the resources available to the experiment have been insufficient to meet ATLAS simulation needs over the past few years as the volume of data from the LHC has grown. The problem willbe even more severe for the next LHC phases. High Luminosity LHC will be a multiexabyte challenge where the envisaged Storage and Compute needs are a factor 10 to 100 above the expected technology evolution. The High Energy Physics (HEP) community needs to evolve current computing and data organization models in order to introduce changes in the way it uses and manages the infrastructure, focused on optimizations to bring performance and efficiency not forgetting simplification of operations. In this paper we highlight recent R&D; projects in HEP related to data lake prototype, federated data storage and data carousel.
Copyright/License © The Authors (License: CC-BY-4.0)

Corresponding record in: Inspire

 Record created 2020-03-29, last modified 2020-04-01

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