https://doi.org/10.1051/epjconf/202022601007
Enabling Data Intensive Science on Supercomputers for High Energy Physics R&D Projects in HL-LHC Era
1
Brookhaven National Laboratory,
NY,
USA
2
Argonne National Laboratory,
IL,
USA
3
European Particle Physics Laboratory (CERN),
Geneva,
Switzerland
4
University of Texas in Arlington,
TX,
USA
5
Josef Stefan Institute,
Ljubljana,
Slovenia
6
Petersburg Nuclear Physics Institute NRC “Kurchatov Institute”,
Gatchina,
Russia
7
Joint Institute of Nuclear Research,
Dubna,
Russia
8
Oak Ridge National Laboratory,
TN,
USA
9
Saint-Petersburg State University,
St. Petersburg,
Russia
10
Plekhanov Russian University of Economics,
Moscow,
Russia
Published online: 20 January 2020
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
