Machine learning method for energy reduction by utilizing dynamic mixed precision on GPU-based supercomputers-Reference-Cited by-同舟云学术

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Machine learning method for energy reduction by utilizing dynamic mixed precision on GPU-based supercomputers

Published:2018-04-30 Issue:6 Volume:31 Page:e4644
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Rojek Krzysztof¹^ORCID

Affiliation:

1. Częstochowa University of Technology; Częstochowa Poland

Funder

Narodowe Centrum Nauki

Swiss National Supercomputing Center

Politechnika Czestochowska

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/cpe.4644/fullpdf

Reference37 articles.

1. Energy profile of rollback-recovery strategies in high performance computing;Meneses;Parallel Comput,2014

2. DVFS-control techniques for dense linear algebra operations on multi-core processors;Alonso;Comput Sci Res Dev,2012

3. Profile-driven automated mixed precision;Ralph;CoRR,2016

4. Rojek K Szustak L Parallelization of EULAG model on multicore architectures with GPU accelerators 2011 Toruń, Poland

5. NVIDIA http://www.nvidia.com 2017

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1. Chemical Mixing Simulations with Integrated AI Accelerator;Computational Science – ICCS 2023;2023

2. Impact of Reduced and Mixed-Precision on the Efficiency of a Multi-GPU Platform on CFD Applications;Computational Science and Its Applications – ICCSA 2022 Workshops;2022

3. Reducing numerical precision preserves classification accuracy in Mondrian Forests;2021 IEEE International Conference on Big Data (Big Data);2021-12-15

4. Tools for Reduced Precision Computation;ACM Computing Surveys;2021-03-31

5. CFD code adaptation to the FPGA architecture;The International Journal of High Performance Computing Applications;2020-11-10

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