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Distributed Analysis within the LHC computing Grid

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Elmsheuser, J. (2007). Distributed Analysis within the LHC computing Grid.

The distributed data analysis using Grid resources is one of the funda- mental applications in high energy physics to be addressed and realized before the start of LHC data taking. The needs to manage the resources are very high. In every experiment up to a thousand physicist will be submitting analysis jobs into the Grid. Appropriate user interfaces and helper applications have to be made available to assure that all users can use the Grid without too much expertise in Grid technology. These tools enlarge the number of Grid users from a few production adminis- trators to potentially all participating physicists. The GANGA job management system (, devel- oped as a common project between the ATLAS and LHCb experiments provides and integrates these kind of tools. GANGA provides a sim- ple and consistent way of preparing, organizing and executing analysis tasks within the experiment analysis framework, implemented through a plug-in system. It allows trivial switching between running test jobs on a local batch system and running large-scale analyzes on the Grid, hiding Grid technicalities. We will be reporting on the plug-ins and our experiences of distributed data analysis using GANGA within the ATLAS experiment and the EGEE/LCG infrastructure. The integration and interaction with the ATLAS data management system DQ2/DDM into GANGA is a key functionality. In combination with the job splitting mechanism large amounts of analysis jobs can be sent to the locations of data following the ATLAS computing model. GANGA supports tasks of user analysis with reconstructed data and small scale production of Monte Carlo data.