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Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters

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Τίτλος: Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters
Συγγραφείς: Mantovani, Filippo, Calore, Enrico
Συνεισφορές: Barcelona Supercomputing Center
Στοιχεία εκδότη: MDPI
Έτος έκδοσης: 2018
Συλλογή: Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge
Θεματικοί όροι: Àrees temàtiques de la UPC::Informàtica, High performance computing, Cluster analysis--Data processing, Performance analysis tools, Power drain, Energy to solution, Paraver, GPU, Cluster, High-Performance computing, Supercomputadors, Computació distribuïda
Περιγραφή: 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. ; 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 Reviewed ; Postprint (published version)
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: 14 p.; application/pdf
Γλώσσα: English
Relation: http://www.mdpi.com/2079-9268/8/2/13; info:eu-repo/grantAgreement/EC/FP7/288777/EU/Mont-Blanc, European scalable and power efficient HPC platform based on low-power embedded technology/MONT-BLANC; info:eu-repo/grantAgreement/EC/FP7/610402/EU/Mont-Blanc 2, European scalable and power efficient HPC platform based on low-power embedded technology/MONT-BLANC 2; info:eu-repo/grantAgreement/EC/H2020/671697/EU/Mont-Blanc 3, European scalable and power efficient HPC platform based on low-power embedded technology/Mont-Blanc 3; https://hdl.handle.net/2117/117020
DOI: 10.3390/jlpea8020013
Διαθεσιμότητα: https://hdl.handle.net/2117/117020
https://doi.org/10.3390/jlpea8020013
Rights: http://creativecommons.org/licenses/by-nc-nd/4.0/es/ ; Open Access ; Attribution-NonCommercial-NoDerivs 4.0 Spain
Αριθμός Καταχώρησης: edsbas.B9F23CE
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  Data: Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters
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  Data: <searchLink fieldCode="AR" term="%22Mantovani%2C+Filippo%22">Mantovani, Filippo</searchLink><br /><searchLink fieldCode="AR" term="%22Calore%2C+Enrico%22">Calore, Enrico</searchLink>
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  Data: Barcelona Supercomputing Center
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  Data: Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge
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  Data: 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. ; 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 Reviewed ; Postprint (published version)
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  Data: 10.3390/jlpea8020013
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      – TitleFull: Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters
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