DIRAC: reliable data management for LHCb

Smith, Andrew C.; Tsaregorodtsev, A.

doi:10.1088/1742-6596/119/6/062045

Cited by 13 publications

(2 citation statements)

References 9 publications

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“…Using the API classes it is easy to write small scripts or applications to manage user jobs [3] and data processing with metadata [6] and provenance [7] management for HTC. These scripts can be used in the client side as PaaS or encapsulated in the server side, for example in a VRE.…”

Section: Dirac Python Apimentioning

confidence: 99%

Powering Distributed Applications with DIRAC Engine

Mendez¹,

Ramo²,

Diaz³

et al. 2014

Proceedings of International Symposium on Grids and Clouds (ISGC) 2014 — PoS(ISGC2014)

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DIRAC has evolved from a framework for distributed computing matters operated by the final user, to a platform that can also be used from third-party Virtual Research Environments (VRE), Science Gateways (SG) or distributed computing applications. Therefore, DIRAC has become a platform providing distributed computing interoperability and high level services for science. For this purpose, DIRAC supports several Application Programming Interfaces (API) designed for a versatile programming model in different developing scenarios. This paper presents such scenarios categorized by the location of the API client. Thus, a first scenario is an application server powered by DIRAC services. Second, a web browser running embedded DIRAC calls, for dynamic web client composition. Third scenario is a DIRAC Platform as a Service (PaaS) powering distributed applications with High Throughput Computing (HTC) on the client side. This paper describes the underlying DIRAC architecture and the main functionalities of each API and developing scenario. Overall DIRAC is a coherent toolkit that enables a wide range of component developments like service for scientific applications, web plugs or PaaS agents.

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Section: Dirac Python Apimentioning

confidence: 99%

Powering Distributed Applications with DIRAC Engine

Mendez¹,

Ramo²,

Diaz³

et al. 2014

Proceedings of International Symposium on Grids and Clouds (ISGC) 2014 — PoS(ISGC2014)

View full text Add to dashboard Cite

show abstract

“…DIRAC manages data distribution from the LHCb detector to mass storage at CERN, CERN Advance STORage (CASTOR [6]), and from there to all LHCb Tier1s, 1 based on the site share of the data [7]. DIRAC also takes care of all required processing steps to make this data available for physics studies: calibration, reconstruction, pre-selection, merging, etc.…”

Section: Introductionmentioning

confidence: 99%

Belle-DIRAC Setup for Using Amazon Elastic Compute Cloud

et al. 2011

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The Distributed Infrastructure with Remote Agent Control (DIRAC) software framework allows a user community to manage computing activities in a distributed environment. DIRAC has been developed within the Large Hadron Collider Beauty (LHCb) collaboration. After successful usage over several years, it is the final solution adopted by the experiment. The Belle experiment at the Japanese High Energy Accelerator Research Organization (KEK) has the purpose of studying matter/anti-matter asymmetries using B mesons. During its lifetime, Belle detector has collected about 5,000 terabytes of real and simulated data. The analysis of this data requires an enormous amount of computing intensive Monte Carlo simulation. The Belle II experiment, which recently published its technical design report, will produce 50 times more data. Therefore it is interested to determine if commercial computing clouds can reduce the total cost of the experiment's computing solution. This paper describes the setup prepared to evaluate the performance and cost of this approach using real 2010 simulation tasks of the Belle experiment. The setup has been developed using DIRAC as the overall management tool to control both the tasks to be executed and the deployment of virtual machines using the Amazon Elastic Compute Cloud as service provider. At the same time, DIRAC is also used to monitor the execution, collect the necessary statistical data, and finally upload the results of the simulation to Belle resources on the Grid. The results of a first test using over 2000 days of cpu time show that over 90% efficiency in the use of the resources can easily be achieved.

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A classification of file placement and replication methods on grids

Liu

Glatard

2013

Future Generation Computer Systems

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DIRAC: reliable data management for LHCb

Cited by 13 publications

References 9 publications

Powering Distributed Applications with DIRAC Engine

Powering Distributed Applications with DIRAC Engine

Belle-DIRAC Setup for Using Amazon Elastic Compute Cloud

A classification of file placement and replication methods on grids

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