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dc.contributor.authorŽivanovič, Darko
dc.contributor.authorRadulovic, Milan
dc.contributor.authorLlort, German
dc.contributor.authorZaragoza, David
dc.contributor.authorStrassburg, Janko
dc.contributor.authorCarpenter, Paul M.
dc.contributor.authorRadojkovic, Petar
dc.contributor.authorAyguadé Parra, Eduard
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2016-12-07T13:29:45Z
dc.date.issued2016
dc.identifier.citationZivanovic, D., Radulovic, M., Llort, G., Zaragoza, D., Strassburg, J., Carpenter, P., Radojkovic, P., Ayguade, E. Large-memory nodes for energy efficient high-performance computing. A: International Symposium on Memory Systems. "MEMSYS 2016: proceedings of the Second Intaernational Symposium on Memory Systems: Alexandria, VA, USA: October 03-06, 2016". Alexandria, VA: Association for Computing Machinery (ACM), 2016, p. 3-9.
dc.identifier.isbn978-1-4503-4305-3
dc.identifier.urihttp://hdl.handle.net/2117/97864
dc.description.abstractEnergy consumption is by far the most important contributor to HPC cluster operational costs, and it accounts for a significant share of the total cost of ownership. Advanced energy-saving techniques in HPC components have received significant research and development effort, but a simple measure that can dramatically reduce energy consumption is often overlooked. We show that, in capacity computing, where many small to medium-sized jobs have to be solved at the lowest cost, a practical energy-saving approach is to scale-in the application on large-memory nodes. We evaluate scaling-in; i.e. decreasing the number of application processes and compute nodes (servers) to solve a fixed-sized problem, using a set of HPC applications running in a production system. Using standard-memory nodes, we obtain average energy savings of 36%, already a huge figure. We show that the main source of these energy savings is a decrease in the node-hours (node_hours = #nodes x exe_time), which is a consequence of the more efficient use of hardware resources. Scaling-in is limited by the per-node memory capacity. We therefore consider using large-memory nodes to enable a greater degree of scaling-in. We show that the additional energy savings, of up to 52%, mean that in many cases the investment in upgrading the hardware would be recovered in a typical system lifetime of less than five years.
dc.format.extent7 p.
dc.language.isoeng
dc.publisherAssociation for Computing Machinery (ACM)
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshHigh performance computing
dc.subject.otherComputer systems organization
dc.subject.otherDistributed architectures
dc.subject.otherHardware
dc.subject.otherPower and energy
dc.titleLarge-memory nodes for energy efficient high-performance computing
dc.typeConference report
dc.subject.lemacCàlcul intensiu (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.1145/2989081.2989083
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://dl.acm.org/citation.cfm?id=2989083
dc.rights.accessOpen Access
local.identifier.drac19329420
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/SEV-2015-0493
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2015-65316-P
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/671578/EU/European Exascale Processor Memory Node Design/ExaNoDe
dc.relation.projectidinfo:eu-repo/grantAgreement/SVP-2014-068501
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2015-65316-P
dc.date.lift10000-01-01
local.citation.authorZivanovic, D.; Radulovic, M.; Llort, G.; Zaragoza, D.; Strassburg, J.; Carpenter, P.; Radojkovic, P.; Ayguade, E.
local.citation.contributorInternational Symposium on Memory Systems
local.citation.pubplaceAlexandria, VA
local.citation.publicationNameMEMSYS 2016: proceedings of the Second Intaernational Symposium on Memory Systems: Alexandria, VA, USA: October 03-06, 2016
local.citation.startingPage3
local.citation.endingPage9


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