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Article

ThunderX2 Performance and Energy-Efficiency for HPC Workloads

1
Istituto Nazionale di Fisica Nucleare, INFN Ferrara, Via Saragat 1, I-44122 Ferrara, Italy
2
Università degli Studi di Ferrara, Via Saragat 1, I-44122 Ferrara, Italy
*
Author to whom correspondence should be addressed.
Computation 2020, 8(1), 20; https://doi.org/10.3390/computation8010020
Received: 3 March 2020 / Revised: 18 March 2020 / Accepted: 20 March 2020 / Published: 23 March 2020
(This article belongs to the Special Issue Energy-Efficient Computing on Parallel Architectures)
In the last years, the energy efficiency of HPC systems is increasingly becoming of paramount importance for environmental, technical, and economical reasons. Several projects have investigated the use of different processors and accelerators in the quest of building systems able to achieve high energy efficiency levels for data centers and HPC installations. In this context, Arm CPU architecture has received a lot of attention given its wide use in low-power and energy-limited applications, but server grade processors have appeared on the market just recently. In this study, we targeted the Marvell ThunderX2, one of the latest Arm-based processors developed to fit the requirements of high performance computing applications. Our interest is mainly focused on the assessment in the context of large HPC installations, and thus we evaluated both computing performance and energy efficiency, using the ERT benchmark and two HPC production ready applications. We finally compared the results with other processors commonly used in large parallel systems and highlight the characteristics of applications which could benefit from the ThunderX2 architecture, in terms of both computing performance and energy efficiency. Pursuing this aim, we also describe how ERT has been modified and optimized for ThunderX2, and how to monitor power drain while running applications on this processor. View Full-Text
Keywords: ThunderX2; Arm; HPC; performance; energy efficiency ThunderX2; Arm; HPC; performance; energy efficiency
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MDPI and ACS Style

Calore, E.; Gabbana, A.; Schifano, S.F.; Tripiccione, R. ThunderX2 Performance and Energy-Efficiency for HPC Workloads. Computation 2020, 8, 20. https://doi.org/10.3390/computation8010020

AMA Style

Calore E, Gabbana A, Schifano SF, Tripiccione R. ThunderX2 Performance and Energy-Efficiency for HPC Workloads. Computation. 2020; 8(1):20. https://doi.org/10.3390/computation8010020

Chicago/Turabian Style

Calore, Enrico, Alessandro Gabbana, Sebastiano F. Schifano, and Raffaele Tripiccione. 2020. "ThunderX2 Performance and Energy-Efficiency for HPC Workloads" Computation 8, no. 1: 20. https://doi.org/10.3390/computation8010020

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