Academic Journal
Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters
| Τίτλος: | Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters |
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| 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 |
| Βάση Δεδομένων: | BASE |
| DOI: | 10.3390/jlpea8020013 |
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