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dc.contributor.authorDe Sandes, Edans
dc.contributor.authorMiranda Álamo, Guillermo
dc.contributor.authorDe Melo, Alba Cristina Magalhaes Alves
dc.contributor.authorMartorell Bofill, Xavier
dc.contributor.authorAyguadé Parra, Eduard
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2014-11-19T15:16:48Z
dc.date.created2014
dc.date.issued2014
dc.identifier.citationDe Sandes, E. [et al.]. CUDAlign 3.0: Parallel biological sequence comparison in large GPU clusters. A: IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. "2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2014): Chicago, Illinois: USA, 26-29 May 2014". Chicago, IL: Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 160-169.
dc.identifier.isbn978-1-4799-2785-2
dc.identifier.urihttp://hdl.handle.net/2117/24766
dc.description.abstractThis paper proposes and evaluates a parallel strategy to execute the e+ xact Smith-Waterman (SW) biological sequence comparison algorithm for huge DNA sequences in multi-GPU platforms. In our strategy, the computation of a single huge SW matrix is spread over multiple GPUs, which communicate border elements to the neighbour, using a circular buffer mechanism. We also provide a method to predict the execution time and speedup of a comparison, given the number of the GPUs and the sizes of the sequences. The results obtained with a large multi-GPU environment show that our solution is scalable when varying the sizes of the sequences and/or the number of GPUs and that our prediction method is accurate. With our proposal, we were able to compare the largest human chromosome with its homologous chimpanzee chromosome (249 Millions of Base Pairs (MBP) x 228 MBP) using 64 GPUs, achieving 1.7 TCUPS (Tera Cells Updated per Second). As far as we know, this is the largest comparison ever done using the Smith-Waterman algorithm.
dc.format.extent10 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::Informàtica::Arquitectura de computadors::Arquitectures paral·leles
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject.lcshBioinformatics
dc.subject.lcshParallel programming (Computer science)
dc.subject.otherBiological sequence comparison
dc.subject.otherGPU
dc.subject.otherSmith-Waterman
dc.titleCUDAlign 3.0: Parallel biological sequence comparison in large GPU clusters
dc.typeConference report
dc.subject.lemacBioinformàtica
dc.subject.lemacProgramació en paral·lel (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.1109/CCGrid.2014.18
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6846451
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac15230052
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorDe Sandes, E.; Miranda, G.; De Melo, A.; Martorell, X.; Ayguade, E.
local.citation.contributorIEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
local.citation.pubplaceChicago, IL
local.citation.publicationName2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2014): Chicago, Illinois: USA, 26-29 May 2014
local.citation.startingPage160
local.citation.endingPage169


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Attribution-NonCommercial-NoDerivs 3.0 Spain
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