CERN Accelerating science

ATLAS Note
Report number	ATL-SOFT-PROC-2017-006
Title	Production experience with the ATLAS Event Service
Author(s)	Benjamin, Douglas (Duke University, Department of Physics) ; Calafiura, Paolo (Lawrence Berkeley National Laboratory and University of California, Berkeley) ; Childers, John Taylor (Argonne National Laboratory) ; De, Kaushik (The University of Texas at Arlington) ; Guan, Wen (Department of Physics, University of Wisconsin) ; Maeno, Tadashi (Brookhaven National Laboratory (BNL)) ; Nilsson, Paul (Brookhaven National Laboratory (BNL)) ; Tsulaia, Vakhtang (Lawrence Berkeley National Laboratory and University of California, Berkeley) ; van Gemmeren, Peter (Argonne National Laboratory) ; Wenaus, Torre (Brookhaven National Laboratory (BNL))
Corporate Author(s)	The ATLAS collaboration
Collaboration	ATLAS Collaboration
Publication	2017
Imprint	07 Jan 2017
Number of pages	8
In:	J. Phys.: Conf. Ser. 898 (2017) 062002
In:	22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016, San Francisco, Usa, 10 - 14 Oct 2016, pp.062002
DOI	10.1088/1742-6596/898/6/062002
Subject category	Particle Physics - Experiment
Accelerator/Facility, Experiment	CERN LHC ; ATLAS
Free keywords	Data processing workflows and frameworks/pipelines ; Distributed workload management ; Distributed data handling
Abstract	The ATLAS Event Service (AES) has been designed and implemented for efficient running of ATLAS production workflows on a variety of computing platforms, ranging from conventional Grid sites to opportunistic, often short-lived resources, such as spot market commercial clouds, supercomputers and volunteer computing. The Event Service architecture allows real time delivery of fine grained workloads to running payload applications which process dispatched events or event ranges and immediately stream the outputs to highly scalable Object Stores. Thanks to its agile and flexible architecture the AES is currently being used by grid sites for assigning low priority workloads to otherwise idle computing resources; similarly harvesting HPC resources in an efficient back-fill mode; and massively scaling out to the 50-100k concurrent core level on the Amazon spot market to efficiently utilize those transient resources for peak production needs. Platform ports in development include ATLAS@Home (BOINC) and the Google Compute Engine, and a growing number of HPC platforms. After briefly reviewing the concept and the architecture of the Event Service, we will report the status and experience gained in AES commissioning and production operations on supercomputers, and our plans for extending ES application beyond Geant4 simulation to other workflows, such as reconstruction and data analysis.
Copyright/License	publication: (License: CC-BY-3.0)

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