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Publication . Article . 2017

The READEX formalism for automatic tuning for energy efficiency

Joseph Schuchart; Michael Gerndt; Per Gunnar Kjeldsberg; Michael Lysaght; David Horák; Lubomír Říha; Andreas Gocht; +14 Authors
Open Access
English
Published: 10 Jan 2017
Publisher: Springer Verlag
Abstract

Energy efficiency is an important aspect of future exascale systems, mainly due to rising energy cost. Although High performance computing (HPC) applications are compute centric, they still exhibit varying computational characteristics in different regions of the program, such as compute-, memory-, and I/O-bound code regions. Some of today’s clusters already offer mechanisms to adjust the system to the resource requirements of an application, e.g., by controlling the CPU frequency. However, manually tuning for improved energy efficiency is a tedious and painstaking task that is often neglected by application developers. The European Union’s Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing) aims at developing a tools-aided approach for improved energy efficiency of current and future HPC applications. To reach this goal, the READEX project combines technologies from two ends of the compute spectrum, embedded systems and HPC, constituting a split design-time/runtime methodology. From the HPC domain, the Periscope Tuning Framework (PTF) is extended to perform dynamic auto-tuning of fine-grained application regions using the systems scenario methodology, which was originally developed for improving the energy efficiency in embedded systems. This paper introduces the concepts of the READEX project, its envisioned implementation, and preliminary results that demonstrate the feasibility of this approach. © The Author(s) 2017. This article is published with open access at Springerlink.com. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Subjects by Vocabulary

Microsoft Academic Graph classification: Automatic tuning Computer communication networks Computer science Formalism (philosophy) Theoretical computer science Efficient energy use Mathematical economics Creative commons License

Subjects

Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Numerical Analysis, Theoretical Computer Science, Software, automatic tuning, energy efficiency, dynamic behaviour, dynamic tuning, parallel computing

Funded by
EC| READEX
Project
READEX
Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
  • Funder: European Commission (EC)
  • Project Code: 671657
  • Funding stream: H2020 | RIA
Validated by funder
,
EC| READEX
Project
READEX
Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
  • Funder: European Commission (EC)
  • Project Code: 671657
  • Funding stream: H2020 | RIA
Validated by funder
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