Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasks

Autores
Petrocelli, David; De Giusti, Armando Eduardo; Naiouf, Marcelo
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Compute-heavy workloads are currently run on Hybrid HPC structures using x86 CPUs and GPUs from Intel, AMD, or NVidia, which have extremely high energy and financial costs. However, thanks to the incredible progress made on CPUs and GPUs based on the ARM architecture and their ubiquity in today’s mobile devices, it’s possible to conceive of a low-cost solution for our world’s data processing needs. Every year ARM-based mobile devices become more powerful, efficient, and come in ever smaller packages with ever growing storage. At the same time, smartphones waste these capabilities at night while they’re charging. This represents billions of idle devices whose processing power is not being utilized. For that reason, the objective of this paper is to evaluate and develop a hybrid, distributed, scalable, and redundant platform that allows for the utilization of these idle devices through a cloud-based administration service. The system would allow for massive improvements in terms of efficiency and cost for com-pute-heavy workload. During the evaluation phase, we were able to establish savings in power and cost significant enough to justify exploring it as a serious alternative to traditional computing architectures.
Instituto de Investigación en Informática
Materia
Ciencias Informáticas
Smartphones
Distributed computing methodologies
Cloud computing
Mobile computing
Collaborative Computing
Android
ARM
HPC
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/80351

id SEDICI_d8dd3b3fb4de4fcb2553268c15f2d1d0
oai_identifier_str oai:sedici.unlp.edu.ar:10915/80351
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasksPetrocelli, DavidDe Giusti, Armando EduardoNaiouf, MarceloCiencias InformáticasSmartphonesDistributed computing methodologiesCloud computingMobile computingCollaborative ComputingAndroidARMHPCCompute-heavy workloads are currently run on Hybrid HPC structures using x86 CPUs and GPUs from Intel, AMD, or NVidia, which have extremely high energy and financial costs. However, thanks to the incredible progress made on CPUs and GPUs based on the ARM architecture and their ubiquity in today’s mobile devices, it’s possible to conceive of a low-cost solution for our world’s data processing needs. Every year ARM-based mobile devices become more powerful, efficient, and come in ever smaller packages with ever growing storage. At the same time, smartphones waste these capabilities at night while they’re charging. This represents billions of idle devices whose processing power is not being utilized. For that reason, the objective of this paper is to evaluate and develop a hybrid, distributed, scalable, and redundant platform that allows for the utilization of these idle devices through a cloud-based administration service. The system would allow for massive improvements in terms of efficiency and cost for com-pute-heavy workload. During the evaluation phase, we were able to establish savings in power and cost significant enough to justify exploring it as a serious alternative to traditional computing architectures.Instituto de Investigación en Informática2019-06info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf16-27http://sedici.unlp.edu.ar/handle/10915/80351enginfo:eu-repo/semantics/altIdentifier/isbn/978-3-030-27713-0info:eu-repo/semantics/reference/doi/10.1007/978-3-030-27713-0info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:47:03Zoai:sedici.unlp.edu.ar:10915/80351Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:47:03.418SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasks
title Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasks
spellingShingle Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasks
Petrocelli, David
Ciencias Informáticas
Smartphones
Distributed computing methodologies
Cloud computing
Mobile computing
Collaborative Computing
Android
ARM
HPC
title_short Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasks
title_full Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasks
title_fullStr Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasks
title_full_unstemmed Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasks
title_sort Hybrid Elastic ARM&Cloud HPC Collaborative Platform for generic tasks
dc.creator.none.fl_str_mv Petrocelli, David
De Giusti, Armando Eduardo
Naiouf, Marcelo
author Petrocelli, David
author_facet Petrocelli, David
De Giusti, Armando Eduardo
Naiouf, Marcelo
author_role author
author2 De Giusti, Armando Eduardo
Naiouf, Marcelo
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Smartphones
Distributed computing methodologies
Cloud computing
Mobile computing
Collaborative Computing
Android
ARM
HPC
topic Ciencias Informáticas
Smartphones
Distributed computing methodologies
Cloud computing
Mobile computing
Collaborative Computing
Android
ARM
HPC
dc.description.none.fl_txt_mv Compute-heavy workloads are currently run on Hybrid HPC structures using x86 CPUs and GPUs from Intel, AMD, or NVidia, which have extremely high energy and financial costs. However, thanks to the incredible progress made on CPUs and GPUs based on the ARM architecture and their ubiquity in today’s mobile devices, it’s possible to conceive of a low-cost solution for our world’s data processing needs. Every year ARM-based mobile devices become more powerful, efficient, and come in ever smaller packages with ever growing storage. At the same time, smartphones waste these capabilities at night while they’re charging. This represents billions of idle devices whose processing power is not being utilized. For that reason, the objective of this paper is to evaluate and develop a hybrid, distributed, scalable, and redundant platform that allows for the utilization of these idle devices through a cloud-based administration service. The system would allow for massive improvements in terms of efficiency and cost for com-pute-heavy workload. During the evaluation phase, we were able to establish savings in power and cost significant enough to justify exploring it as a serious alternative to traditional computing architectures.
Instituto de Investigación en Informática
description Compute-heavy workloads are currently run on Hybrid HPC structures using x86 CPUs and GPUs from Intel, AMD, or NVidia, which have extremely high energy and financial costs. However, thanks to the incredible progress made on CPUs and GPUs based on the ARM architecture and their ubiquity in today’s mobile devices, it’s possible to conceive of a low-cost solution for our world’s data processing needs. Every year ARM-based mobile devices become more powerful, efficient, and come in ever smaller packages with ever growing storage. At the same time, smartphones waste these capabilities at night while they’re charging. This represents billions of idle devices whose processing power is not being utilized. For that reason, the objective of this paper is to evaluate and develop a hybrid, distributed, scalable, and redundant platform that allows for the utilization of these idle devices through a cloud-based administration service. The system would allow for massive improvements in terms of efficiency and cost for com-pute-heavy workload. During the evaluation phase, we were able to establish savings in power and cost significant enough to justify exploring it as a serious alternative to traditional computing architectures.
publishDate 2019
dc.date.none.fl_str_mv 2019-06
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/80351
url http://sedici.unlp.edu.ar/handle/10915/80351
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-3-030-27713-0
info:eu-repo/semantics/reference/doi/10.1007/978-3-030-27713-0
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
16-27
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1842260342798811136
score 13.13397