Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation. Fox, G.; Glazier, J. A.; Kadupitiya, J.; Jadhao, V.; Kim, M.; Qiu, J.; Sluka, J. P.; Somogyi, E.; Marathe, M.; Adiga, A.; Chen, J.; Beckstein, O.; and Jha, S. In 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pages 422–429, May, 2019. ISSN: null
doi  abstract   bibtex   
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.
@inproceedings{fox_learning_2019,
	title = {Learning {Everywhere}: {Pervasive} {Machine} {Learning} for {Effective} {High}-{Performance} {Computation}},
	shorttitle = {Learning {Everywhere}},
	doi = {10.1109/IPDPSW.2019.00081},
	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.},
	booktitle = {2019 {IEEE} {International} {Parallel} and {Distributed} {Processing} {Symposium} {Workshops} ({IPDPSW})},
	author = {Fox, Geoffrey and Glazier, James A. and Kadupitiya, J.C.S. and Jadhao, Vikram and Kim, Minje and Qiu, Judy and Sluka, James P. and Somogyi, Endre and Marathe, Madhav and Adiga, Abhijin and Chen, Jiangzhuo and Beckstein, Oliver and Jha, Shantenu},
	month = may,
	year = {2019},
	note = {ISSN: null},
	keywords = {Biological system modeling, Computational modeling, Data models, Effective Performance, Forecasting, ML approaches, Machine learning, Machine learning driven HPC, Mathematical model, Predictive models, data intensive methodologies, general description, high-performance computation, learning (artificial intelligence), learning everywhere paradigm, learning methodologies, learning methods, open software systems, parallel processing, performance improvements, pervasive machine learning, simulation based approaches, traditional HPC, traditional performance, ubiquitous computing},
	pages = {422--429},
}
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