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On-line detection of large-scale parallel application's structure
dc.contributor.author | Llort Sánchez, Germán |
dc.contributor.author | González García, Juan |
dc.contributor.author | Servat, Harald |
dc.contributor.author | Giménez Lucas, Judit |
dc.contributor.author | Labarta Mancho, Jesús José |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.date.accessioned | 2014-10-08T14:48:38Z |
dc.date.created | 2010 |
dc.date.issued | 2010 |
dc.identifier.citation | Llort, G. [et al.]. On-line detection of large-scale parallel application's structure. A: IEEE International Parallel and Distributed Processing Symposium. "IEEE International Symposium on Parallel & Distributed Processing: IPDPS 2010: Atlanta, Georgia, USA: 19-23 April 2010". Atlanta, GA: Institute of Electrical and Electronics Engineers (IEEE), 2010, p. 1-10. |
dc.identifier.isbn | 978-1-4244-6441-8 |
dc.identifier.uri | http://hdl.handle.net/2117/24309 |
dc.description.abstract | With larger and larger systems being constantly deployed, trace-based performance analysis of parallel applications has become a daunting task. Even if the amount of performance data gathered per single process is small, traces rapidly become unmanageable when merging together the information collected from all processes. In general, an e cient analysis of such a large volume of data is subject to a previous ltering step that directs the analyst's attention towards what is meaningful to understand the observed application behavior. Furthermore, the iterative nature of most scienti c applications usually ends up producing repetitive information. Discarding irrelevant data aims at reducing both the size of traces, and the time required to perform the analysis and deliver results. In this paper, we present an on-line analysis framework that relies on clustering techniques to intelligently select the most relevant information to understand how does the application behave, while keeping the trace volume at a reasonable size. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors::Arquitectures distribuïdes |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
dc.subject.lcsh | Parallel programming (Computer science) |
dc.subject.lcsh | Cluster analysis |
dc.subject.other | Parallel processing |
dc.subject.other | Pattern clustering |
dc.title | On-line detection of large-scale parallel application's structure |
dc.type | Conference report |
dc.subject.lemac | Programació en paral·lel (Informàtica) |
dc.subject.lemac | Anàlisi de conglomerats |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.identifier.doi | 10.1109/IPDPS.2010.5470350 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5470350 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 15017093 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Llort, G.; González, J.; Servat, H.; Gimenez, J.; Labarta, J. |
local.citation.contributor | IEEE International Parallel and Distributed Processing Symposium |
local.citation.pubplace | Atlanta, GA |
local.citation.publicationName | IEEE International Symposium on Parallel & Distributed Processing: IPDPS 2010: Atlanta, Georgia, USA: 19-23 April 2010 |
local.citation.startingPage | 1 |
local.citation.endingPage | 10 |