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dc.contributor.authorValero-Lara, Pedro
dc.contributor.authorMartinez-Perez, Ivan
dc.contributor.authorPeña, Antonio J.
dc.contributor.authorMartorell Bofill, Xavier
dc.contributor.authorSirvent, Raul
dc.contributor.authorLabarta Mancho, Jesús José
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2017-07-19T10:59:26Z
dc.date.available2017-07-19T10:59:26Z
dc.date.issued2017
dc.identifier.citationValero-Lara, P., Martinez-Perez, I., Peña, A., Martorell, X., Sirvent, R., Labarta, J. cuHinesBatch: solving multiple hines systems on GPUs Human Brain Project. "Procedia computer science", 2017, vol. 108, p. 566-575.
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/2117/106615
dc.description.abstractThe simulation of the behavior of the Human Brain is one of the most important challenges today in computing. The main problem consists of finding efficient ways to manipulate and compute the huge volume of data that this kind of simulations need, using the current technology. In this sense, this work is focused on one of the main steps of such simulation, which consists of computing the Voltage on neurons’ morphology. This is carried out using the Hines Algorithm. Although this algorithm is the optimum method in terms of number of operations, it is in need of non-trivial modifications to be efficiently parallelized on NVIDIA GPUs. We proposed several optimizations to accelerate this algorithm on GPU-based architectures, exploring the limitations of both, method and architecture, to be able to solve efficiently a high number of Hines systems (neurons). Each of the optimizations are deeply analyzed and described. To evaluate the impact of the optimizations on real inputs, we have used 6 different morphologies in terms of size and branches. Our studies have proven that the optimizations proposed in the present work can achieve a high performance on those computations with a high number of neurons, being our GPU implementations about 4× and 8× faster than the OpenMP multicore implementation (16 cores), using one and two K80 NVIDIA GPUs respectively. Also, it is important to highlight that these optimizations can continue scaling even when dealing with number of neurons.
dc.description.sponsorshipThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 720270 (HBP SGA1), from the Spanish Ministry of Economy and Competitiveness under the project Computación de Altas Prestaciones VII (TIN2015-65316-P) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d’Execució Paral·lels (2014-SGR-1051). We thank the support of NVIDIA through the BSC/UPC NVIDIA GPU Center of Excellence. Antonio J. Peña is cofinanced by the Spanish Ministry of Economy and Competitiveness under Juan de la Cierva fellowship number IJCI-2015-23266.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherElsevier
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.subject.lcshParallel programming (Computer science)
dc.subject.lcshBrain -- Data processing
dc.subject.otherHuman brain
dc.subject.otherNeuron
dc.subject.otherHines algorithm
dc.subject.otherParallel computing
dc.subject.otherGPUs
dc.subject.otherMulticore
dc.subject.otherCUDA
dc.titlecuHinesBatch: solving multiple hines systems on GPUs Human Brain Project
dc.typeArticle
dc.subject.lemacProgramació en paral·lel (Informàtica)
dc.subject.lemacCervell -- Informàtica
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.1016/j.procs.2017.05.145
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S1877050917307172#fn1
dc.rights.accessOpen Access
local.identifier.drac21184320
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/720270/EU/Human Brain Project Specific Grant Agreement 1/HBP SGA1
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO//IJCI-2015-23266/ES/IJCI-2015-23266/
local.citation.authorValero-Lara, P.; Martinez-Perez, I.; Peña, A.; Martorell, X.; Sirvent, R.; Labarta, J.
local.citation.publicationNameProcedia computer science
local.citation.volume108
local.citation.startingPage566
local.citation.endingPage575


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