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Trends Of CPU, GPU and FPGA for high-performance computing

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Summary:Floating-point computing with more than one TFLOP of peak performance is already a reality in recent Field-Programmable Gate Arrays (FPGA). General-Purpose Graphics Processing Units (GPGPU) and recent many-core CPUs have also taken advantage of the recent technological innovations in integrated circuit (IC) design and had also dramatically improved their peak performances. In this paper, we compare the trends of these computing architectures for high-performance computing and survey these platforms in the execution of algorithms belonging to different scientific application domains. Trends in peak performance, power consumption and sustained performances, for particular applications, show that FPGAs are increasing the gap to GPUs and many-core CPUs moving them away from high-performance computing with intensive floating-point calculations. FPGAs become competitive for custom floating-point or fixed-point representations, for smaller input sizes of certain algorithms, for combinational logic problems and parallel map-reduce problems. © 2014 Technical University of Munich (TUM).
Main Authors:Véstias, Mário
Other Authors:Neto, Horácio
Subject:High-Performance Computing Field-Programmable Gate Arrays (FPGA)
Year:2014
Country:Portugal
Document type:conference output
Access type:restricted access
Associated institution:Instituto Politécnico de Lisboa
Language:English
Origin:Repositório Científico do Instituto Politécnico de Lisboa
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author Véstias, Mário
author2 Neto, Horácio
author2_role author
author_facet Véstias, Mário
Neto, Horácio
author_role author
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country_str PT
creators_json_txt [{\"Person.name\":\"Véstias, Mário\",\"Person.identifier.orcid\":\"0000-0001-8556-4507\"},{\"Person.name\":\"Neto, Horácio\"}]
datacite.contributors.contributor.contributorName.fl_str_mv RCIPL
datacite.creators.creator.creatorName.fl_str_mv Véstias, Mário
Neto, Horácio
datacite.date.Accepted.fl_str_mv 2014-10-01T00:00:00Z
datacite.date.available.fl_str_mv 2015-08-19T09:59:06Z
datacite.date.embargoed.fl_str_mv 2015-08-19T09:59:06Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_16ec
datacite.subjects.subject.fl_str_mv High-Performance Computing
Field-Programmable Gate Arrays (FPGA)
datacite.titles.title.fl_str_mv Trends Of CPU, GPU and FPGA for high-performance computing
dc.contributor.none.fl_str_mv RCIPL
dc.creator.none.fl_str_mv Véstias, Mário
Neto, Horácio
dc.date.Accepted.fl_str_mv 2014-10-01T00:00:00Z
dc.date.available.fl_str_mv 2015-08-19T09:59:06Z
dc.date.embargoed.fl_str_mv 2015-08-19T09:59:06Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/10400.21/4840
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv IEEE - Institute of Electrical and Electronics Engineers Inc.
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.subject.none.fl_str_mv High-Performance Computing
Field-Programmable Gate Arrays (FPGA)
dc.title.fl_str_mv Trends Of CPU, GPU and FPGA for high-performance computing
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_c94f
description Floating-point computing with more than one TFLOP of peak performance is already a reality in recent Field-Programmable Gate Arrays (FPGA). General-Purpose Graphics Processing Units (GPGPU) and recent many-core CPUs have also taken advantage of the recent technological innovations in integrated circuit (IC) design and had also dramatically improved their peak performances. In this paper, we compare the trends of these computing architectures for high-performance computing and survey these platforms in the execution of algorithms belonging to different scientific application domains. Trends in peak performance, power consumption and sustained performances, for particular applications, show that FPGAs are increasing the gap to GPUs and many-core CPUs moving them away from high-performance computing with intensive floating-point calculations. FPGAs become competitive for custom floating-point or fixed-point representations, for smaller input sizes of certain algorithms, for combinational logic problems and parallel map-reduce problems. © 2014 Technical University of Munich (TUM).
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person_str_mv Véstias, Mário
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spelling engIEEE - Institute of Electrical and Electronics Engineers Inc.porFloating-point computing with more than one TFLOP of peak performance is already a reality in recent Field-Programmable Gate Arrays (FPGA). General-Purpose Graphics Processing Units (GPGPU) and recent many-core CPUs have also taken advantage of the recent technological innovations in integrated circuit (IC) design and had also dramatically improved their peak performances. In this paper, we compare the trends of these computing architectures for high-performance computing and survey these platforms in the execution of algorithms belonging to different scientific application domains. Trends in peak performance, power consumption and sustained performances, for particular applications, show that FPGAs are increasing the gap to GPUs and many-core CPUs moving them away from high-performance computing with intensive floating-point calculations. FPGAs become competitive for custom floating-point or fixed-point representations, for smaller input sizes of certain algorithms, for combinational logic problems and parallel map-reduce problems. © 2014 Technical University of Munich (TUM).application/pdfporTrends Of CPU, GPU and FPGA for high-performance computingPersonalVéstias, MárioDSpacehttp://dspace.org/items/a7d22b29-c961-45ac-bc09-cd5e1002f1e8DSpacehttp://dspace.org/items/a7d22b29-c961-45ac-bc09-cd5e1002f1e8VéstiasMárioCiência IDhttps://www.ciencia-id.pt4717-C2C7-3F2CORCIDhttp://orcid.org0000-0001-8556-4507Researcher IDhttps://www.researcherid.comH-9953-2012Scopus Author IDhttps://www.scopus.com14525867300Neto, HorácioHostingInstitutionOrganizationalRCIPLe-mailmailto:rcaap@sp.ipl.ptrcaap@sp.ipl.ptISBNIsPartOf978-300044645-0DOIIsPartOf10.1109/FPL.2014.69274832015-08-19T09:59:06Z2014-102014-10-01T00:00:00ZHandlehttp://hdl.handle.net/10400.21/4840http://purl.org/coar/access_right/c_16ecrestricted accessHigh-Performance ComputingField-Programmable Gate Arrays (FPGA)1321657 bytesFundação para a Ciência e a TecnologiaStrategic Project - LA 21 - 2013-20146817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871other research producthttp://purl.org/coar/resource_type/c_c94fconference objecthttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://repositorio.ipl.pt/bitstreams/7ecf934d-ade4-4c14-8de1-247186e0601d/downloadConference Digest - 24th International Conference on Field Programmable Logic and Applications, FPLNew York
spellingShingle Trends Of CPU, GPU and FPGA for high-performance computing
Véstias, Mário
High-Performance Computing
Field-Programmable Gate Arrays (FPGA)
status SINGLETON
subject.fl_str_mv High-Performance Computing
Field-Programmable Gate Arrays (FPGA)
title Trends Of CPU, GPU and FPGA for high-performance computing
title_full Trends Of CPU, GPU and FPGA for high-performance computing
title_fullStr Trends Of CPU, GPU and FPGA for high-performance computing
title_full_unstemmed Trends Of CPU, GPU and FPGA for high-performance computing
title_short Trends Of CPU, GPU and FPGA for high-performance computing
title_sort Trends Of CPU, GPU and FPGA for high-performance computing
topic High-Performance Computing
Field-Programmable Gate Arrays (FPGA)
topic_facet High-Performance Computing
Field-Programmable Gate Arrays (FPGA)
url http://hdl.handle.net/10400.21/4840
visible 1