Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics-Reference-Cited by-同舟云学术

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Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics

Published:2022 Issue: Volume: Page:119-138
ISSN:1865-0929
Container-title:Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation
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Author:

Gainaru Ana,Ganyushin Dmitry,Xie Bing,Kurc Tahsin,Saltz Joel,Oral Sarp,Podhorszki Norbert,Poeschel Franz,Huebl Axel,Klasky Scott

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-96498-6_7

Reference42 articles.

1. Baghban, A., Kahani, M., Nazari, M.A., Ahmadi, M.H., Yan, W.-M.: Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of CNT/water nanofluid flows through coils. Int. J. Heat Mass Transf. 128, 825–835 (2019)

2. Bei, Z., et al.: RFHOC: a random-forest approach to auto-tuning hadoop’s configuration. IEEE Trans. Parallel Distrib. Syst. 27(5), 1470–1483 (2016)

3. Cummings, J., et al.: EFFIS: an end-to-end framework for fusion integrated simulation. In: 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing, pp. 428–434 (2010)

4. Deelman, E., et al.: The Pegasus workflow management system: translational computer science in practice. J. Comput. Sci. 52, 101200 (2021). Funding Acknowledgments: NSF 1664162

5. Dieleman, S., Willett, K.W., Dambre, J.: Rotation-invariant convolutional neural networks for galaxy morphology prediction. Mon. Not. R. Astron. Soc. 450(2), 1441–1459 (2015)

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1. High-Throughput Computing: Case Study of Medical Image Processing Applications;Proceedings of the 14th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures;2024-06-03

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