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dc.contributor.authorBoccali, T.
dc.contributor.authorCameron, D.
dc.contributor.authorCardo, N.
dc.contributor.authorConciatore, D.
dc.contributor.authorDi Girolamo, A.
dc.contributor.authorDissertori, G.
dc.contributor.authorFernandez, P.
dc.contributor.authorFilipcic, A.
dc.contributor.authorGila, M.
dc.contributor.authorGrab, C.
dc.contributor.authorElmsheuser, J.
dc.contributor.authorJankauskas, V.
dc.date.accessioned2021-09-20T17:41:09Z
dc.date.available2021-09-20T17:41:09Z
dc.date.issued2021-02-08
dc.identifier.urihttps://hdl.handle.net/1721.1/131966
dc.description.abstractAbstract The prompt reconstruction of the data recorded from the Large Hadron Collider (LHC) detectors has always been addressed by dedicated resources at the CERN Tier-0. Such workloads come in spikes due to the nature of the operation of the accelerator and in special high load occasions experiments have commissioned methods to distribute (spill-over) a fraction of the load to sites outside CERN. The present work demonstrates a new way of supporting the Tier-0 environment by provisioning resources elastically for such spilled-over workflows onto the Piz Daint Supercomputer at CSCS. This is implemented using containers, tuning the existing batch scheduler and reinforcing the scratch file system, while still using standard Grid middleware. ATLAS, CMS and CSCS have jointly run selected prompt data reconstruction on up to several thousand cores on Piz Daint into a shared environment, thereby probing the viability of the CSCS high performance computer site as on demand extension of the CERN Tier-0, which could play a role in addressing the future LHC computing challenges for the high luminosity LHC.en_US
dc.publisherSpringer International Publishingen_US
dc.relation.isversionofhttps://doi.org/10.1007/s41781-020-00052-wen_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceSpringer International Publishingen_US
dc.titleDynamic Distribution of High-Rate Data Processing from CERN to Remote HPC Data Centersen_US
dc.typeArticleen_US
dc.identifier.citationComputing and Software for Big Science. 2021 Feb 08;5(1):7en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Physics
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-02-14T05:13:36Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.embargo.termsN
dspace.date.submission2021-02-14T05:13:36Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Needed


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