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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.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.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.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.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.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/671578/EU/European Exascale Processor Memory Node Design/ExaNoDe
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

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