Abstract

More than one thousand physicists analyse data collected by the ATLAS experiment at the Large Hadron Collider (LHC) at CERN through 150 computing facilities around the world. Efficient distributed analysis requires optimal resource usage and the interplay of several factors: robust grid and software infrastructures, and system capability to adapt to different workloads. The continuous automatic validation of grid sites and the user support provided by a dedicated team of expert shifters have been proven to provide a solid distributed analysis system for ATLAS users. Typical user workflows on the grid, and their associated metrics, are discussed. Measurements of user job performance and typical requirements are also shown.

Highlights

  • The ATLAS distributed computing systemThe ATLAS [1] computing model is based on distributed resources [2]. Collider and simulated

  • Monte Carlo data samples are centrally produced, and distributed over more than 150 computing centers spread around the world

  • The first Large Hadron Collider (LHC) data reconstruction is done at the Tier-0, the computing facilities at CERN and at the Wigner Research Centre for Physics, and at the Tier1s, primary computing facilities worldwide

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Summary

The ATLAS distributed computing system

The ATLAS [1] computing model is based on distributed resources [2]. Collider and simulated. DxAOD formats have a size less than 1% of the original xAOD for data, while typical reduction factors on MC samples are of a few percent. The DxAOD format (and the original xAOD) is readable by both ROOT and Athena, the ATLAS reconstruction and analysis framework [4]. The final DxAOD datasets are distributed to the Tier-1s, the Tier-2s, and additional analysis facilities, the Tier-3s. All these activities are referred to as central production, since it is centrally managed and organized. The PanDA workload management system [5] is used to submit tasks to ATLAS grid resources. Users and production managers submit tasks, which are split into individual jobs by PanDA, to optimize the usage of distributed resources. ATLAS users may submit tasks using either PanDA or Ganga [11] client tools

Distributed analysis for LHC Run 2
Distributed analysis performance
Future developments
Findings
Conclusions
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