Keynote at HPSC 2013 at NTU, Taiwan: Systematizing tuning of computer systems using crowdsourcing and statistics

Grigori Fursin 1
1 GRAND-LARGE - Global parallel and distributed computing
LRI - Laboratoire de Recherche en Informatique, LIFL - Laboratoire d'Informatique Fondamentale de Lille, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Continuing innovation in science and technology is vital for our society and requires ever increasing computational resources. However, delivering such resources has become intolerably complex, ad-hoc, costly and error prone due to an enormous number of available design and optimization choices combined with the complex interactions between all software and hardware components. Auto-tuning, run-time adaptation and machine learning based approaches have been demonstrating good promise to address above challenges for more than a decade but are still far from the widespread production use due to unbearably long exploration and training times, lack of a common experimental methodology, and lack of public repositories for unified data collection, analysis and mining.
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Contributeur : Grigori Fursin <>
Soumis le : mardi 29 octobre 2013 - 07:00:10
Dernière modification le : jeudi 5 avril 2018 - 12:30:12

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Grigori Fursin. Keynote at HPSC 2013 at NTU, Taiwan: Systematizing tuning of computer systems using crowdsourcing and statistics. HPSC - Conference on Advanced Topics and Auto Tuning in High Performance and Scientific Computing - 2013, Mar 2013, Taipei, Taiwan. 〈hal-00819000〉

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