Saving Energy Using the READEX Methodology

Madhura Kumaraswamy (), Anamika Chowdhury, Andreas Gocht, Jan Zapletal, Kai Diethelm, Lubomir Riha, Marie-Christine Sawley, Michael Gerndt, Nico Reissmann, Ondrej Vysocky, Othman Bouizi, Per Gunnar Kjeldsberg, Ramon Carreras, Robert Schöne, Umbreen Sabir Mian, Venkatesh Kannan and Wolfgang E. Nagel
Additional contact information
Madhura Kumaraswamy: Technical University of Munich, Department of Informatics
Anamika Chowdhury: Technical University of Munich, Department of Informatics
Andreas Gocht: Technische Universität Dresden
Jan Zapletal: VŠB – Technical University of Ostrava, IT4Innovations National Supercomputing Center
Kai Diethelm: Gesellschaft für numerische Simulation GmbH
Lubomir Riha: VŠB – Technical University of Ostrava, IT4Innovations National Supercomputing Center
Marie-Christine Sawley: Intel ExaScale Labs
Michael Gerndt: Technical University of Munich, Department of Informatics
Nico Reissmann: Norwegian University of Science and Technology
Ondrej Vysocky: VŠB – Technical University of Ostrava, IT4Innovations National Supercomputing Center
Othman Bouizi: Intel ExaScale Labs
Per Gunnar Kjeldsberg: Norwegian University of Science and Technology
Ramon Carreras: Irish Centre for High-End Computing
Robert Schöne: Technische Universität Dresden
Umbreen Sabir Mian: Technische Universität Dresden
Venkatesh Kannan: Irish Centre for High-End Computing
Wolfgang E. Nagel: Technische Universität Dresden

A chapter in Tools for High Performance Computing 2018 / 2019, 2021, pp 27-53 from Springer

Abstract: Abstract With today’s top supercomputers consuming several megawatts of power, optimization of energy consumption has become one of the major challenges on the road to exascale computing. The EU Horizon 2020 project READEX provides a tools-aided auto-tuning methodology to dynamically tune HPC applications for energy-efficiency. READEX is a two-step methodology, consisting of the design-time analysis and runtime tuning stages. At design-time, READEX exploits application dynamism using the $${readex\_intraphase}$$ r e a d e x _ i n t r a p h a s e and the $${readex\_interphase}$$ r e a d e x _ i n t e r p h a s e tuning plugins, which perform tuning steps, and provide tuning advice in the form of a tuning model. During production runs, the runtime tuning stage reads the tuning model and dynamically switches the settings of the tuning parameters for different application regions. Additionally, READEX also includes a tuning model visualizer and support for tuning application level tuning parameters to improve the result beyond the automatic version. This paper describes the state of the art used in READEX for energy-efficiency auto-tuning for HPC. Energy savings achieved for different proxy benchmarks and production level applications on the Haswell and Broadwell processors highlight the effectiveness of this methodology.

Date: 2021
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DOI: 10.1007/978-3-030-66057-4_2

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