EPJ Web of Conferences
Volume 108, 2016Mathematical Modeling and Computational Physics (MMCP 2015)
|Number of page(s)||12|
|Section||Plenary and Invited Lectures|
|Published online||09 February 2016|
PanDA: Exascale Federation of Resources for the ATLAS Experiment at the LHC
1 University of Texas at Arlington, 502 Yates Street, Arlington, TX 76019-0059, USA
2 Brookhaven National Laboratory, Upton, Long Island, New York 11973, USA
3 Joint Institute for Nuclear Research, Joliot-Curie 6, Dubna, 141980, Russia
a e-mail: firstname.lastname@example.org
Published online: 9 February 2016
After a scheduled maintenance and upgrade period, the world’s largest and most powerful machine – the Large Hadron Collider(LHC) – is about to enter its second run at unprecedented energies. In order to exploit the scientific potential of the machine, the experiments at the LHC face computational challenges with enormous data volumes that need to be analysed by thousand of physics users and compared to simulated data. Given diverse funding constraints, the computational resources for the LHC have been deployed in a worldwide mesh of data centres, connected to each other through Grid technologies.
The PanDA (Production and Distributed Analysis) system was developed in 2005 for the ATLAS experiment on top of this heterogeneous infrastructure to seamlessly integrate the computational resources and give the users the feeling of a unique system. Since its origins, PanDA has evolved together with upcoming computing paradigms in and outside HEP, such as changes in the networking model, Cloud Computing and HPC. It is currently running steadily up to 200 thousand simultaneous cores (limited by the available resources for ATLAS), up to two million aggregated jobs per day and processes over an exabyte of data per year. The success of PanDA in ATLAS is triggering the widespread adoption and testing by other experiments. In this contribution we will give an overview of the PanDA components and focus on the new features and upcoming challenges that are relevant to the next decade of distributed computing workload management using PanDA.
© Owned by the authors, published by EDP Sciences, 2016
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.
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