Mark Slater

Key developments of the Ganga task-management framework

Ganga is the main end-user distributed analysis tool for the ATLAS and LHCb experiments and provides the foundation layer for the HammerCloud system, used by the LHC experiments for validation and stress testing of their numerous distributed computing facilities. Here we illustrate recent developments and demonstrate how tools that were initially developed for a specific user community have been migrated into the Ganga core, and so can be exploited by a wider user-base. Similarly, examples will be given where Ganga components have been adapted for use by communities in their custom analysis packages.

Reinforcing user data analysis with Ganga in the LHC era: scalability, monitoring and user-support

Ganga is a grid job submission and management system widely used in the ATLAS and LHCb experiments and several other communities in the context of the EGEE project. The particle physics communities have entered the LHC operation era which brings new challenges for user data analysis: a strong growth in the number of users and jobs is already noticeable. Current work in the Ganga project is focusing on dealing with these challenges. In recent Ganga releases the support for the pilot job based grid systems Panda and Dirac of the ATLAS and LHCb experiment respectively have been strengthened. A more scalable job repository architecture, which allows efficient storage of many thousands of jobs in XML or several database formats, was recently introduced. A better integration with monitoring systems, including the Dashboard and job execution monitor systems is underway. These will provide comprehensive and easy job monitoring. A simple to use error reporting tool integrated at the Ganga command-line will help to improve user support and debugging user problems. Ganga is a mature, stable and widely-used tool with long-term support from the HEP community. We report on how it is being constantly improved following the user needs for faster and easier distributed data analysis on the grid.

Measurement of D∗±D∗±, D±D± and D±sDs± meson production cross sections in pppp collisions at s√=7s=7 TeV with the ATLAS detector

The production of D∗±, D± and Ds charmed mesons has been measured with the ATLAS detector in pp collisions at √ s = 7 TeV at the LHC, using data corresponding to an integrated luminosity of 280 nb−1. The charmed mesons have been reconstructed in the range of transverse momentum 3.5 < pT(D) < 100 GeV and pseudorapidity |η(D)| < 2.1. The differential cross sections as a function of transverse momentum and pseudorapidity were measured for D∗± and D± production. The next-to-leading-order QCD predictions are consistent with the data in the visible kinematic region within the large theoretical uncertainties. Using the visible D cross sections and an extrapolation to the full kinematic phase space, the strangeness-suppression factor in charm fragmentation, the fraction of charged non-strange D mesons produced in a vector state, and the total cross section of charm production at √ s = 7 TeV were derived. c

User analysis of LHCb data with Ganga

GANGA (http://cern.ch/ganga) is a job-management tool that offers a simple, efficient and consistent user analysis tool in a variety of heterogeneous environments: from local clusters to global Grid systems. Experiment specific plug-ins allow GANGA to be customised for each experiment. For LHCb users GANGA is the officially supported and advertised tool for job submission to the Grid. The LHCb specific plug-ins allow support for end-to-end analysis helping the user to perform his complete analysis with the help of GANGA. This starts with the support for data selection, where a user can select data sets from the LHCb Bookkeeping system. Next comes the set up for large analysis jobs: with tailored plug-ins for the LHCb core software, jobs can be managed by the splitting of these analysis jobs with the subsequent merging of the resulting files. Furthermore, GANGA offers support for Toy Monte-Carlos to help the user tune their analysis. In addition to describing the GANGA architecture, typical usage patterns within LHCb and experience with the updated LHCb DIRAC workload management system are presented.