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dc.contributor.authorCalore, Enrico
dc.contributor.authorMantovani, Filippo
dc.contributor.authorRuiz, Daniel
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2017-08-07T08:19:45Z
dc.date.available2017-08-07T08:19:45Z
dc.date.issued2018
dc.identifier.citationE. Calore, F. Mantovani and D. Ruiz, "Advanced Performance Analysis of HPC Workloads on Cavium ThunderX," 2018 International Conference on High Performance Computing & Simulation (HPCS), Orleans, France, 2018, pp. 375-382. doi: 10.1109/HPCS.2018.00068
dc.identifier.isbn978-1-5386-7879-4
dc.identifier.urihttp://hdl.handle.net/2117/107063
dc.description.abstractThe interest towards Arm based platforms as HPC solutions increased significantly during the last 5 years. In this paper we show that, in contrast to the early days of pioneer tests, several application performance analysis techniques can now be applied also to Arm based SoCs. To show the possibilities offered by the available tools, we provide as an example, the analysis of a Lattice Boltzmann HPC production code, highly optimized for several architectures and now ported also to Armv8. We tested it on a system based on a production silicon, Cavium CN8890 SoC. In particular, as performance analysis tools we adopt Extrae and Paraver, making use of the PAPI support, initially developed by us for the ThunderX platform, and now available also upstream. The contribution of this paper is twofold: first, we demonstrate that performance analysis tools available on standard HPC platforms, independently from the CPU providers, are nowadays available also for Arm SoCs; second, we actually optimize an HPC application for this platforms, showing similarities with other architectures.
dc.description.sponsorshipThe 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 [15], 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”. Cavium Inc. has kindly supported this research providing access to documentation and platforms.
dc.format.extent12 p.
dc.language.isoeng
dc.publisherIEEE
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica
dc.subject.lcshHigh performance computing
dc.subject.otherARM architecture
dc.subject.otherHigh performance computing
dc.subject.otherPAPI
dc.titleAdvanced Performance Analysis of HPC Workloads on Cavium ThunderX
dc.typeConference lecture
dc.subject.lemacSupercomputadors
dc.identifier.doi10.1109/HPCS.2018.00068
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8514373
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/288777/EU/Mont-Blanc, European scalable and power efficient HPC platform based on low-power embedded technology/MONT-BLANC
dc.relation.projectidinfo: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
dc.relation.projectidinfo: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
local.citation.publicationName2018 International Conference on High Performance Computing & Simulation (HPCS)
local.citation.startingPage1
local.citation.startingPage375
local.citation.endingPage12
local.citation.endingPage382


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