DIRAC: A Community Grid Solution

@inproceedings{Tsaregorodtsev2008DIRACAC,
  title={DIRAC: A Community Grid Solution},
  author={Andrei Tsaregorodtsev and Marianne Bargiotti and N. Brook and Adria Casajus Ramo and Giovanni Castellani and Ph. Charpentier and C. Cioffi and Joel Closier and Ricardo Graciani Diaz and G. Kuznetsov and Ying Yi Li and Rajalakshmi Nandakumar and S. K. Paterson and Roberto Santinelli and Arthur Cosslett Smith and M. Seco Miguelez and Severiana Jimenez},
  year={2008}
}
The DIRAC system was developed in order to provide a complete solution for using the distributed computing resources of the LHCb experiment at CERN for data production and analysis. It allows a concurrent use of over 10K CPUs and 10M file replicas distributed over many tens of sites. The sites can be part of a Computing Grid such as WLCG or standalone computing clusters all integrated in a single management structure. DIRAC is a generic system with the LHCb specific functionality incorporated… Expand
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An overview of the DIRAC system, its main components and their interaction with LCG services and resources is presented and the functionality with different types of workload is described. Expand
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The LHCb distributed data analysis system consists of the Ganga job submission front-end and the DIRAC Workload and Data Management System (WMS). Ganga is jointly developed with ATLAS and allows LHCbExpand
DIRAC: Reliable Data Management for LHCb
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This paper presents several examples of mechanisms implemented in the DMS to increase reliability, availability and integrity, highlighting successful design choices and limitations discovered. Expand
DIRAC optimized workload management
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The recent developments to support Monte Carlo simulation, data processing and distributed user analysis in a consistent way across disparate compute resources including individual PCs, local batch systems, and the Worldwide LHC Computing Grid are described. Expand
DIRAC: data production management
TLDR
The LHCb Computing Model is reviewed and the DIRAC components providing the needed functionality to support the Computing Model will be detailed and an evaluation of the preparedness for real data taking will be given. Expand
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