Learning everywhere: Pervasive machine learning for effective high-performance computation

Geoffrey Fox, James Glazier, J. C.S. Kadupitiya, Vikram Jadhao, Minje Kim, Judy Qiu, James P. Sluka, Endre Somogy, Madhav Marathe, Abhijin Adiga, Jiangzhuo Chen, Oliver Beckstein, Shantenu Jha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The convergence of HPC and data intensive methodologies provide a promising approach to major performance improvements. This paper provides a general description of the interaction between traditional HPC and ML approaches and motivates the 'Learning Everywhere' paradigm for HPC. We introduce the concept of 'effective performance' that one can achieve by combining learning methodologies with simulation based approaches, and distinguish between traditional performance as measured by benchmark scores. To support the promise of integrating HPC and learning methods, this paper examines specific examples and opportunities across a series of domains. It concludes with a series of open software systems, methods and infrastructure challenges that the Learning Everywhere paradigm presents.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages422-429
Number of pages8
ISBN (Electronic)9781728135106
DOIs
StatePublished - May 2019
Event33rd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019 - Rio de Janeiro, Brazil
Duration: May 20 2019May 24 2019

Publication series

NameProceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019

Conference

Conference33rd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019
CountryBrazil
CityRio de Janeiro
Period5/20/195/24/19

Keywords

  • Effective Performance
  • Machine learning driven HPC

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Control and Optimization

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  • Cite this

    Fox, G., Glazier, J., Kadupitiya, J. C. S., Jadhao, V., Kim, M., Qiu, J., Sluka, J. P., Somogy, E., Marathe, M., Adiga, A., Chen, J., Beckstein, O., & Jha, S. (2019). Learning everywhere: Pervasive machine learning for effective high-performance computation. In Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019 (pp. 422-429). [8778333] (Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPSW.2019.00081