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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/80351
Ver los metadatos del registro completo
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 |