Machine learning for adaptive many-core machines: a practical approach

The overwhelming data produced everyday and the increasing performance and cost requirements of applications?are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solv...

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Detalles Bibliográficos
Autores principales: Lopes, Noel, Ribeiro, Bernardete
Lenguaje:eng
Publicado: Springer 2015
Materias:
Computing and Computers
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-06938-8
http://cds.cern.ch/record/1967883
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author Lopes, Noel
Ribeiro, Bernardete
author_facet Lopes, Noel
Ribeiro, Bernardete
author_sort Lopes, Noel
collection CERN
description The overwhelming data produced everyday and the increasing performance and cost requirements of applications?are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind.
id cern-1967883
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2015
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spelling cern-19678832021-04-21T20:50:59Zdoi:10.1007/978-3-319-06938-8http://cds.cern.ch/record/1967883engLopes, NoelRibeiro, BernardeteMachine learning for adaptive many-core machines: a practical approachComputing and ComputersThe overwhelming data produced everyday and the increasing performance and cost requirements of applications?are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind.Springeroai:cds.cern.ch:19678832015
spellingShingle Computing and Computers
Lopes, Noel
Ribeiro, Bernardete
Machine learning for adaptive many-core machines: a practical approach
title Machine learning for adaptive many-core machines: a practical approach
title_full Machine learning for adaptive many-core machines: a practical approach
title_fullStr Machine learning for adaptive many-core machines: a practical approach
title_full_unstemmed Machine learning for adaptive many-core machines: a practical approach
title_short Machine learning for adaptive many-core machines: a practical approach
title_sort machine learning for adaptive many-core machines: a practical approach
topic Computing and Computers
url https://dx.doi.org/10.1007/978-3-319-06938-8
http://cds.cern.ch/record/1967883
work_keys_str_mv AT lopesnoel machinelearningforadaptivemanycoremachinesapracticalapproach
AT ribeirobernardete machinelearningforadaptivemanycoremachinesapracticalapproach

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