Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters
Performance analysis tools allow application developers to identify and characterize the inefficiencies that cause performance degradation in their codes, allowing for application optimizations. Due to the increasing interest in the High Performance Computing (HPC) community towards energy-efficienc...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| 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:2117/117020 |
| Acceso en línea: | https://hdl.handle.net/2117/117020 https://dx.doi.org/10.3390/jlpea8020013 |
| Access Level: | acceso abierto |
| Palabra clave: | High performance computing Cluster analysis--Data processing Performance analysis tools Power drain Energy to solution Paraver GPU Cluster High-Performance computing Supercomputadors Computació distribuïda Àrees temàtiques de la UPC::Informàtica |
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Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node ClustersMantovani, Filippo|||0000-0003-3559-4825Calore, EnricoHigh performance computingCluster analysis--Data processingPerformance analysis toolsPower drainEnergy to solutionParaverGPUClusterHigh-Performance computingSupercomputadorsComputació distribuïdaÀrees temàtiques de la UPC::InformàticaPerformance analysis tools allow application developers to identify and characterize the inefficiencies that cause performance degradation in their codes, allowing for application optimizations. Due to the increasing interest in the High Performance Computing (HPC) community towards energy-efficiency issues, it is of paramount importance to be able to correlate performance and power figures within the same profiling and analysis tools. For this reason, we present a performance and energy-efficiency study aimed at demonstrating how a single tool can be used to collect most of the relevant metrics. In particular, we show how the same analysis techniques can be applicable on different architectures, analyzing the same HPC application on a high-end and a low-power cluster. The former cluster embeds Intel Haswell CPUs and NVIDIA K80 GPUs, while the latter is made up of NVIDIA Jetson TX1 boards, each hosting an Arm Cortex-A57 CPU and an NVIDIA Tegra X1 Maxwell GPU.The research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] and Horizon 2020 under the Mont-Blanc projects [17], grant agreements n. 288777, 610402 and 671697. E.C. was partially founded by “Contributo 5 per mille assegnato all’Università degli Studi di Ferrara-dichiarazione dei redditi dell’anno 2014”. We thank the University of Ferrara and INFN Ferrara for the access to the COKA Cluster. We warmly thank the BSC tools group, supporting us for the smooth integration and test of our setup within Extrae and Paraver.Peer ReviewedMDPI20182018-05-0420182018-05-08journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/117020https://dx.doi.org/10.3390/jlpea8020013reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 288777 Mont-Blanc, European scalable and power efficient HPC platform based on low-power embedded technologyEuropean Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 610402 Mont-Blanc 2, European scalable and power efficient HPC platform based on low-power embedded technologyEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 671697 Mont-Blanc 3, European scalable and power efficient HPC platform based on low-power embedded technologyopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 4.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/4.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1170202026-05-27T15:37:01Z |
| dc.title.none.fl_str_mv |
Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters |
| title |
Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters |
| spellingShingle |
Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters Mantovani, Filippo|||0000-0003-3559-4825 High performance computing Cluster analysis--Data processing Performance analysis tools Power drain Energy to solution Paraver GPU Cluster High-Performance computing Supercomputadors Computació distribuïda Àrees temàtiques de la UPC::Informàtica |
| title_short |
Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters |
| title_full |
Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters |
| title_fullStr |
Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters |
| title_full_unstemmed |
Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters |
| title_sort |
Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters |
| dc.creator.none.fl_str_mv |
Mantovani, Filippo|||0000-0003-3559-4825 Calore, Enrico |
| author |
Mantovani, Filippo|||0000-0003-3559-4825 |
| author_facet |
Mantovani, Filippo|||0000-0003-3559-4825 Calore, Enrico |
| author_role |
author |
| author2 |
Calore, Enrico |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
High performance computing Cluster analysis--Data processing Performance analysis tools Power drain Energy to solution Paraver GPU Cluster High-Performance computing Supercomputadors Computació distribuïda Àrees temàtiques de la UPC::Informàtica |
| topic |
High performance computing Cluster analysis--Data processing Performance analysis tools Power drain Energy to solution Paraver GPU Cluster High-Performance computing Supercomputadors Computació distribuïda Àrees temàtiques de la UPC::Informàtica |
| description |
Performance analysis tools allow application developers to identify and characterize the inefficiencies that cause performance degradation in their codes, allowing for application optimizations. Due to the increasing interest in the High Performance Computing (HPC) community towards energy-efficiency issues, it is of paramount importance to be able to correlate performance and power figures within the same profiling and analysis tools. For this reason, we present a performance and energy-efficiency study aimed at demonstrating how a single tool can be used to collect most of the relevant metrics. In particular, we show how the same analysis techniques can be applicable on different architectures, analyzing the same HPC application on a high-end and a low-power cluster. The former cluster embeds Intel Haswell CPUs and NVIDIA K80 GPUs, while the latter is made up of NVIDIA Jetson TX1 boards, each hosting an Arm Cortex-A57 CPU and an NVIDIA Tegra X1 Maxwell GPU. |
| publishDate |
2018 |
| dc.date.none.fl_str_mv |
2018 2018-05-04 2018 2018-05-08 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2117/117020 https://dx.doi.org/10.3390/jlpea8020013 |
| url |
https://hdl.handle.net/2117/117020 https://dx.doi.org/10.3390/jlpea8020013 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.relation.none.fl_str_mv |
European Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 288777 Mont-Blanc, European scalable and power efficient HPC platform based on low-power embedded technology European Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 610402 Mont-Blanc 2, European scalable and power efficient HPC platform based on low-power embedded technology European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 671697 Mont-Blanc 3, European scalable and power efficient HPC platform based on low-power embedded technology |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Attribution-NonCommercial-NoDerivs 4.0 Spain http://creativecommons.org/licenses/by-nc-nd/4.0/es/ |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 Attribution-NonCommercial-NoDerivs 4.0 Spain http://creativecommons.org/licenses/by-nc-nd/4.0/es/ |
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openAccess |
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application/pdf |
| dc.publisher.none.fl_str_mv |
MDPI |
| publisher.none.fl_str_mv |
MDPI |
| dc.source.none.fl_str_mv |
reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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Universitat Politècnica de Catalunya (UPC) |
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UPCommons. Portal del coneixement obert de la UPC |
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UPCommons. Portal del coneixement obert de la UPC |
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