CERN Accelerating science

ATLAS Note
Report number	ATL-SOFT-PROC-2015-057
Title	PanDA: Exascale Federation of Resources for the ATLAS Experiment at the LHC
Author(s)	Barreiro Megino, Fernando Harald (The University of Texas at Arlington) ; Caballero-Bejar, Jose (Brookhaven National Laboratory (BNL)) ; De, Kaushik (The University of Texas at Arlington) ; Hover, John (Brookhaven National Laboratory (BNL)) ; Klimentov, Alexei (Brookhaven National Laboratory (BNL)) ; Maeno, Tadashi (Brookhaven National Laboratory (BNL)) ; Nilsson, Paul (Brookhaven National Laboratory (BNL)) ; Oleynik, Danila (Joint Institute for Nuclear Research) ; Padolski, Siarhei (Brookhaven National Laboratory (BNL)) ; Panitkin, Sergey (Brookhaven National Laboratory (BNL)) ; Petrosyan, Artem (Joint Institute for Nuclear Research) ; Wenaus, Torre (Brookhaven National Laboratory (BNL))
Corporate Author(s)	The ATLAS collaboration
Publication	2016
Imprint	13 Jul 2015
Number of pages	12
In:	EPJ Web Conf. 108 (2016) 01001
DOI	10.1051/epjconf/201610801001
Subject category	Particle Physics - Experiment
Accelerator/Facility, Experiment	CERN LHC ; ATLAS
Free keywords	PanDA ; Workload management ; Cloud Computing ; HPC ; Network
Abstract	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.
Copyright/License	Preprint: (License: CC-BY-4.0)

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