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J. Low Power Electron. Appl. 2018, 8(2), 18; https://doi.org/10.3390/jlpea8020018

Software and DVFS Tuning for Performance and Energy-Efficiency on Intel KNL Processors

1
Università degli Studi di Ferrara and INFN, 44122 Ferrara, Italy
2
Bergische Universität Wuppertal, 42119 Wuppertal, Germany
*
Author to whom correspondence should be addressed.
Received: 29 March 2018 / Revised: 25 May 2018 / Accepted: 1 June 2018 / Published: 11 June 2018
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Abstract

Energy consumption of processors and memories is quickly becoming a limiting factor in the deployment of large computing systems. For this reason, it is important to understand the energy performance of these processors and to study strategies allowing their use in the most efficient way. In this work, we focus on the computing and energy performance of the Knights Landing Xeon Phi, the latest Intel many-core architecture processor for HPC applications. We consider the 64-core Xeon Phi 7230 and profile its performance and energy efficiency using both its on-chip MCDRAM and the off-chip DDR4 memory as the main storage for application data. As a benchmark application, we use a lattice Boltzmann code heavily optimized for this architecture and implemented using several different arrangements of the application data in memory (data-layouts, in short). We also assess the dependence of energy consumption on data-layouts, memory configurations (DDR4 or MCDRAM) and the number of threads per core. We finally consider possible trade-offs between computing performance and energy efficiency, tuning the clock frequency of the processor using the Dynamic Voltage and Frequency Scaling (DVFS) technique. View Full-Text
Keywords: energy; KNL; MCDRAM; memory; lattice Boltzmann; HPC; DVFS energy; KNL; MCDRAM; memory; lattice Boltzmann; HPC; DVFS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Calore, E.; Gabbana, A.; Schifano, S.F.; Tripiccione, R. Software and DVFS Tuning for Performance and Energy-Efficiency on Intel KNL Processors. J. Low Power Electron. Appl. 2018, 8, 18.

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