@inproceedings{ddd085cb5bc0471a9d47a4808de39c33,
title = "Learning everywhere: Pervasive machine learning for effective high-performance computation",
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.",
keywords = "Effective Performance, Machine learning driven HPC",
author = "Geoffrey Fox and James Glazier and Kadupitiya, {J. C.S.} and Vikram Jadhao and Minje Kim and Judy Qiu and Sluka, {James P.} and Endre Somogy and Madhav Marathe and Abhijin Adiga and Jiangzhuo Chen and Oliver Beckstein and Shantenu Jha",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 33rd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019 ; Conference date: 20-05-2019 Through 24-05-2019",
year = "2019",
month = may,
doi = "10.1109/IPDPSW.2019.00081",
language = "English (US)",
series = "Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "422--429",
booktitle = "Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019",
address = "United States",
}