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dc.contributor.authorWagner, Michael
dc.contributor.authorKnüpfer, Andreas
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2017-07-26T09:09:54Z
dc.date.available2017-07-26T09:09:54Z
dc.date.issued2017-07-13
dc.identifier.citationWagner, M.; Knüpfer, A. Automatic Adaption of the Sampling Frequency for Detailed Performance Analysis. A: "Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 973-981.
dc.identifier.isbn978-1-5090-6610-0
dc.identifier.urihttp://hdl.handle.net/2117/106848
dc.description.abstractOne of the most urgent challenges in event based performance analysis is the enormous amount of collected data. Combining event tracing and periodic sampling has been a successful approach to allow a detailed event-based recording of MPI communication and a coarse recording of the remaining application with periodic sampling. In this paper, we present a novel approach to automatically adapt the sampling frequency during runtime to the given amount of buffer space, releasing users to find an appropriate sampling frequency themselves. This way, the entire measurement can be kept within a single memory buffer, which avoids disruptive intermediate memory buffer flushes, excessive data volumes, and measurement delays due to slow file system interaction. We describe our approach to sort and store samples based on their order of occurrence in an hierarchical array based on powers of two. Furthermore, we evaluate the feasibility as well as the overhead of the approach with the prototype implementation OTFX based on the Open Trace Format 2, a state-of-the-art Open Source event trace library used by the performance analysis tools Vampir, Scalasca, and Tau.
dc.description.sponsorshipThis work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P.
dc.format.extent9 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria elèctrica
dc.subject.lcshHigh performance computing
dc.subject.lcshReal-time data processing
dc.subject.otherRuntime
dc.subject.otherTools
dc.subject.otherFrequency measurement
dc.subject.otherPerformance analysis
dc.subject.otherLibraries
dc.subject.otherVolume measurement
dc.subject.otherInstruments
dc.titleAutomatic Adaption of the Sampling Frequency for Detailed Performance Analysis
dc.typeConference lecture
dc.subject.lemacSupercomputadors
dc.subject.lemacBases de dades--Gestió
dc.identifier.doi10.1109/CCGRID.2017.43
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7973805/
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2015-65316-P
local.citation.publicationNameProceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
local.citation.startingPage973
local.citation.endingPage981


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain