The LHCb computing data challenge DC06

Nandakumar, R.; Jimenez, S G; Adinolfi, Marta; Bernet, R.; Blouw, J.; Bortolotti, D.; Carbone, A.; Mʼcharek, B.; Perego, D. L.; Pickford, A.; Potterat, C.; Miguelez, M S; Bargiotti, M.; Brook, N. H.; Ramo, A Casajus; Castellani, G.; Charpentier, Philippe; Cioffi, Carmela; Closier, J.; Diaz, R. Graciani; Kuznetsov, G; Paterson, S. K.; Santinelli, R.; Smith, Andrew C.; Tsaregorotsev, A

doi:10.1088/1742-6596/119/7/072023

Cited by 4 publications

(3 citation statements)

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“…On the whole, the Production System showed a very stable performance with an effective job success rate well above 90%. More details on the LHCb production experience can be found in [18]. The DIRAC DC'06 performance was achieved with all the central WMS services, including the job database, running on just one host situated at CERN.…”

Section: Production Experience and Performancementioning

confidence: 99%

DIRAC: a community grid solution

Tsaregorodtsev

Bargiotti

Brook

et al. 2008

J. Phys.: Conf. Ser.

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123

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Abstract. 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 through a number of plug-in modules. It can be easily adapted to the needs of other communities. Special attention is paid to the resilience of the DIRAC components to allow an efficient use of non-reliable resources. The DIRAC production management components provide a framework for building highly automated data production systems including data distribution and data driven workload scheduling. In this paper we give an overview of the DIRAC system architecture and design choices. We show how different components are put together to compose an integrated data processing system including all the aspects of the LHCb experiment -from the MC production and raw data reconstruction to the final user analysis.

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Section: Production Experience and Performancementioning

confidence: 99%

DIRAC: a community grid solution

Tsaregorodtsev

Bargiotti

Brook

et al. 2008

J. Phys.: Conf. Ser.

Self Cite

123

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“…The DIRAC Stager System discussed in Section 5 has been extensively used in LHCb computing exercises during 2007 [13]. The current system is still vulnerable to files being removed from disk cache before the replication/job attempts to access the file.…”

Section: Future Workmentioning

confidence: 99%

DIRAC: reliable data management for LHCb

Smith

Tsaregorodtsev

2008

J. Phys.: Conf. Ser.

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“…Many challenges have been achieved using Grid infrastructures to run production tasks; see, e.g. [23] in Biomedicine or the LHCb computing data challenge [30] in HEP. Although the Grid was initially though for both production and heavy parallel tasks, nowadays parallel execution is still dependent on the specific Grid infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Benefits and requirements of grid computing for climate applications. An example with the community atmospheric model

Fernández-Quiruelas

Fernández

Cofiño

et al. 2011

Environmental Modelling & Software

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Grid computing is nowadays an established technology in fields such as High Energy Physics and Biomedicine, offering an alternative to traditional HPC for several problems; however, it is still an emerging discipline for the climate community and only a few climate applications have been adapted to the Grid to solve particular problems. In this paper we present an up-to-date description of the advantages and limitations of the Grid for climate applications (in particular global circulation models), analyzing the requirements and the new challenges posed to the Grid. In particular, we focus on production-like problems such as sensitivity analysis or ensemble prediction, where a single model is run several times with different parameters, forcing and/or initial conditions. As an illustrative example, we consider the Community Atmospheric Model (CAM) and analyse the advantages and shortcomings of the Grid to perform a sensitivity study of precipitation with SST perturbations in El Niño area, reporting the results obtained with traditional (local cluster) and Grid infrastructures. We conclude that new specific middleware (execution workflow managers) are needed to meet the particular requirements of climate applications (long simulations, check-pointing, etc.). This requires the side-by-side collaboration of IT and climate groups to deploy fully ported applications, such as the CAM for Grid (CAM4G) introduced in this paper.

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The LHCb computing data challenge DC06

Cited by 4 publications

References 0 publications

DIRAC: a community grid solution

DIRAC: a community grid solution

DIRAC: reliable data management for LHCb

Benefits and requirements of grid computing for climate applications. An example with the community atmospheric model

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