Special issue on energy reduction techniques for exa-scale computing: theory and practice

Shajulin Benedict; Michael Gerndt; Siegfried Benkner

doi:10.1007/s00607-017-0570-9

Special issue on energy reduction techniques for exa-scale computing: theory and practice

Shajulin Benedict
, Michael Gerndt
, Siegfried Benkner

Research Group Scientific Computing

Publications: Contribution to journal › Article › Peer Reviewed

Original language	English
Pages (from-to)	725-726
Number of pages	2
Journal	Theory and Practice
Volume	99
DOIs	https://doi.org/10.1007/s00607-017-0570-9
Publication status	Published - Aug 2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Austrian Fields of Science 2012

102023 Supercomputing

Keywords

AutoTuning
Energy Efficiency
Energy Prediction
Tools

Access to Document

10.1007/s00607-017-0570-9

Cite this

@article{3aee9e7d97e5453a8c19d8f16df15645,

title = "Special issue on energy reduction techniques for exa-scale computing: theory and practice",

keywords = "AutoTuning, Energy Efficiency, Energy Prediction, Tools",

author = "Shajulin Benedict and Michael Gerndt and Siegfried Benkner",

note = "Funding Information: Paper 3: A few dynamic regression model based prediction approaches were proposed by Rejitha et al. The approach targeted CUDA based applications on GPGPU architectures. The work was carried out at the HPCCLoud Research Laboratory, India, as part of the Energy Tuning of Scientific Applications (EASE) project—an Indo-Austrian DST-FWF funded project.",

year = "2017",

month = aug,

doi = "10.1007/s00607-017-0570-9",

language = "English",

volume = "99",

pages = "725--726",

journal = "Theory and Practice",

issn = "0741-6156",

}

TY - JOUR

T1 - Special issue on energy reduction techniques for exa-scale computing

T2 - theory and practice

AU - Benedict, Shajulin

AU - Gerndt, Michael

AU - Benkner, Siegfried

N1 - Funding Information: Paper 3: A few dynamic regression model based prediction approaches were proposed by Rejitha et al. The approach targeted CUDA based applications on GPGPU architectures. The work was carried out at the HPCCLoud Research Laboratory, India, as part of the Energy Tuning of Scientific Applications (EASE) project—an Indo-Austrian DST-FWF funded project.

PY - 2017/8

Y1 - 2017/8

KW - AutoTuning

KW - Energy Efficiency

KW - Energy Prediction

KW - Tools

UR - https://www.scopus.com/pages/publications/85026450925

U2 - 10.1007/s00607-017-0570-9

DO - 10.1007/s00607-017-0570-9

M3 - Article

SN - 0741-6156

VL - 99

SP - 725

EP - 726

JO - Theory and Practice

JF - Theory and Practice

ER -

Special issue on energy reduction techniques for exa-scale computing: theory and practice

UN SDGs

Austrian Fields of Science 2012

Keywords

Access to Document

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Cite this