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dc.contributor.authorLe Fèvre, Valentin
dc.contributor.authorBautista-Gomez, Leonardo
dc.contributor.authorUnsal, Osman
dc.contributor.authorCasas, Marc
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2019-06-04T13:35:04Z
dc.date.available2019-06-04T13:35:04Z
dc.date.issued2019-02-14
dc.identifier.citationLe Fèvre, V. [et al.]. Approximating a Multi-Grid Solver. A: "2018 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)". IEEE, 2019, p. 97-107.
dc.identifier.isbn978-1-7281-0182-8
dc.identifier.urihttp://hdl.handle.net/2117/133927
dc.description.abstractMulti-grid methods are numerical algorithms used in parallel and distributed processing. The main idea of multigrid solvers is to speedup the convergence of an iterative method by reducing the problem to a coarser grid a number of times. Multi-grid methods are widely exploited in many application domains, thus it is important to improve their performance and energy efficiency. This paper aims to reach this objective based on the following observation: Given that the intermediary steps do not require full accuracy, it is possible to save time and energy by reducing precision during some steps while keeping the final result within the targeted accuracy. To achieve this goal, we first introduce a cycle shape different from the classic V-cycle used in multi-grid solvers. Then, we propose to dynamically change the floating-point precision used during runtime according to the accuracy needed for each intermediary step. Our evaluation considering a state-of-the-art multi-grid solver implementation demonstrates that it is possible to trade temporary precision for time to completion without hurting the quality of the final result. In particular, we are able to reach the same accuracy results as with full double-precision while gaining between 15% and 30% execution time improvement.
dc.description.sponsorshipThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 708566 (DURO). The European Commission is not liable for any use that might be made of the information contained therein. This work has been supported by the Spanish Government (Severo Ochoa grant SEV2015-0493)
dc.format.extent11 p.
dc.language.isoeng
dc.publisherIEEE
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
dc.subject.lcshSupercomputers
dc.subject.lcshParallel processing (Electronic computers)
dc.subject.otherMulti-grid
dc.subject.otherAlgorithms
dc.subject.otherParallel processing
dc.subject.otherIterative method
dc.subject.otherApproximate computing
dc.titleApproximating a Multi-Grid Solver
dc.typeConference lecture
dc.subject.lemacSupercomputadors
dc.subject.lemacProcessament en paral·lel (Ordinadors)
dc.identifier.doi10.1109/PMBS.2018.8641651
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8641651
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/708566/EU/DURO: Deep-memory Ubiquity, Reliability and Optimization/DURO
local.citation.publicationName2018 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)
local.citation.startingPage97
local.citation.endingPage107


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Llevat que s'hi indiqui el contrari, els continguts d'aquesta obra estan subjectes a la llicència de Creative Commons : Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya