1. Enabling Data Intensive Science on Supercomputers for High Energy Physics R&D Projects in HL-LHC Era.
- Author
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Adam, Gh., Buša, J., Hnatič, M., Klimentov, Alexei, Benjamin, Douglas, Di Girolamo, Alessandro, De, Kaushik, Elmsheuser, Johannes, Filipcic, Andrej, Kiryanov, Andrey, Oleynik, Danila, Wells, Jack C., Zarochentsev, Andrey, and Zhao, Xin
- Subjects
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SUPERCOMPUTERS , *PARTICLE physics , *DATA management , *PROTOTYPE (Linguistics) , *COMPUTER systems - Abstract
The ATLAS experiment at CERN's Large Hadron Collider uses theWorldwide 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 will be 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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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