CUsched: multiprogrammed workload scheduling on GPU architectures

Graphic Processing Units (GPUs) are currently widely used in High Performance Computing (HPC) applications to speed-up the execution of massively-parallel codes. GPUs are well-suited for such HPC environments because applications share a common characteristic with the gaming codes GPUs were designed...

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Autores: Tanasic, Ivan, Gelado Fernandez, Isaac, Cabezas, Javier, Navarro, Nacho|||0000-0003-3637-4568, Ramírez Bellido, Alejandro, Valero Cortés, Mateo|||0000-0003-2917-2482
Tipo de recurso: informe técnico
Fecha de publicación:2013
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/110728
Acceso en línea:https://hdl.handle.net/2117/110728
Access Level:acceso abierto
Palabra clave:High performance computing
Parallel processing (Electronic computers)
GPU
Scheduling
Graphic Processing Units
Càlcul intensiu (Informàtica)
Processament en paral·lel (Ordinadors)
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
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network_acronym_str ES
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repository_id_str
spelling CUsched: multiprogrammed workload scheduling on GPU architecturesTanasic, IvanGelado Fernandez, IsaacCabezas, JavierNavarro, Nacho|||0000-0003-3637-4568Ramírez Bellido, AlejandroValero Cortés, Mateo|||0000-0003-2917-2482High performance computingParallel processing (Electronic computers)GPUSchedulingGraphic Processing UnitsCàlcul intensiu (Informàtica)Processament en paral·lel (Ordinadors)Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadorsGraphic Processing Units (GPUs) are currently widely used in High Performance Computing (HPC) applications to speed-up the execution of massively-parallel codes. GPUs are well-suited for such HPC environments because applications share a common characteristic with the gaming codes GPUs were designed for: only one application is using the GPU at the same time. Although, minimal support for multi-programmed systems exist, modern GPUs do not allow resource sharing among different processes. This lack of support restricts the usage of GPUs in desktop and mobile environment to a small amount of applications (e.g., games and multimedia players). In this paper we study the multi-programming support available in current GPUs, and show how such support is not sufficient. We propose a set of hardware extensions to the current GPU architectures to efficiently support multi-programmed GPU workloads, allowing concurrent execution of codes from different user processes. We implement several hardware schedulers on top of these extensions and analyze the behaviour of different work scheduling algorithms using system wide and per process metrics.20132013-01-0120172017-11-16reporthttp://purl.org/coar/resource_type/c_93fcVoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/2117/110728reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1107282026-05-27T15:37:01Z
dc.title.none.fl_str_mv CUsched: multiprogrammed workload scheduling on GPU architectures
title CUsched: multiprogrammed workload scheduling on GPU architectures
spellingShingle CUsched: multiprogrammed workload scheduling on GPU architectures
Tanasic, Ivan
High performance computing
Parallel processing (Electronic computers)
GPU
Scheduling
Graphic Processing Units
Càlcul intensiu (Informàtica)
Processament en paral·lel (Ordinadors)
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
title_short CUsched: multiprogrammed workload scheduling on GPU architectures
title_full CUsched: multiprogrammed workload scheduling on GPU architectures
title_fullStr CUsched: multiprogrammed workload scheduling on GPU architectures
title_full_unstemmed CUsched: multiprogrammed workload scheduling on GPU architectures
title_sort CUsched: multiprogrammed workload scheduling on GPU architectures
dc.creator.none.fl_str_mv Tanasic, Ivan
Gelado Fernandez, Isaac
Cabezas, Javier
Navarro, Nacho|||0000-0003-3637-4568
Ramírez Bellido, Alejandro
Valero Cortés, Mateo|||0000-0003-2917-2482
author Tanasic, Ivan
author_facet Tanasic, Ivan
Gelado Fernandez, Isaac
Cabezas, Javier
Navarro, Nacho|||0000-0003-3637-4568
Ramírez Bellido, Alejandro
Valero Cortés, Mateo|||0000-0003-2917-2482
author_role author
author2 Gelado Fernandez, Isaac
Cabezas, Javier
Navarro, Nacho|||0000-0003-3637-4568
Ramírez Bellido, Alejandro
Valero Cortés, Mateo|||0000-0003-2917-2482
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv High performance computing
Parallel processing (Electronic computers)
GPU
Scheduling
Graphic Processing Units
Càlcul intensiu (Informàtica)
Processament en paral·lel (Ordinadors)
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
topic High performance computing
Parallel processing (Electronic computers)
GPU
Scheduling
Graphic Processing Units
Càlcul intensiu (Informàtica)
Processament en paral·lel (Ordinadors)
Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
description Graphic Processing Units (GPUs) are currently widely used in High Performance Computing (HPC) applications to speed-up the execution of massively-parallel codes. GPUs are well-suited for such HPC environments because applications share a common characteristic with the gaming codes GPUs were designed for: only one application is using the GPU at the same time. Although, minimal support for multi-programmed systems exist, modern GPUs do not allow resource sharing among different processes. This lack of support restricts the usage of GPUs in desktop and mobile environment to a small amount of applications (e.g., games and multimedia players). In this paper we study the multi-programming support available in current GPUs, and show how such support is not sufficient. We propose a set of hardware extensions to the current GPU architectures to efficiently support multi-programmed GPU workloads, allowing concurrent execution of codes from different user processes. We implement several hardware schedulers on top of these extensions and analyze the behaviour of different work scheduling algorithms using system wide and per process metrics.
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-01-01
2017
2017-11-16
dc.type.none.fl_str_mv report
http://purl.org/coar/resource_type/c_93fc
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/110728
url https://hdl.handle.net/2117/110728
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869415411001327616
score 15.301603

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