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PRIONN: Predicting Runtime and IO using Neural Networks.

M. II, S. Herbein, T. Gamblin, A. Moody, D. Ahn, and M. Taufer. ICPP, page 46:1-46:12. ACM, (2018)

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Moving Volunteer Computing towards Knowledge-Constructed, Dynamically-Adaptive Modeling and Scheduling.M. Taufer, A. Kerstens, T. Estrada, D. Flores, R. Zamudio, P. Teller, R. Armen, and C. III. IPDPS, page 1-8. IEEE, (2007)Benchmarking the Performance of Scientific Applications with Irregular I/O at the Extreme Scale.S. Herbein, S. Klasky, and M. Taufer. ICPP Workshops, page 292-301. IEEE Computer Society, (2014)Molecular Dynamics Simulations on Cray Clusters using the SCIDDLE-PVM environment.P. Arbenz, M. Billeter, P. Güntert, P. Luginbühl, M. Taufer, and U. von Matt. PVM, volume 1156 of Lecture Notes in Computer Science, page 142-149. Springer, (1996)Free energetics of carbon nanotube association in aqueous inorganic NaI salt solutions: Temperature effects using all-atom molecular dynamics simulations.S. Ou, D. Cui, M. Wezowicz, M. Taufer, and S. Patel. Journal of Computational Chemistry, 36 (16): 1196-1212 (2015)Scalability and resource usage of an OLAP benchmark on clusters of PCs.M. Taufer, T. Stricker, and R. Weber. SPAA, page 83-94. ACM, (2002)Network Quality of Service in Docker Containers.A. Dusia, Y. Yang, and M. Taufer. CLUSTER, page 527-528. IEEE Computer Society, (2015)On the Need for Reproducible Numerical Accuracy through Intelligent Runtime Selection of Reduction Algorithms at the Extreme Scale.D. Chapp, T. Johnston, and M. Taufer. CLUSTER, page 166-175. IEEE Computer Society, (2015)HYPPO: A Hybrid, Piecewise Polynomial Modeling Technique for Non-Smooth Surfaces.T. Johnston, C. Zanin, and M. Taufer. SBAC-PAD, page 26-33. IEEE Computer Society, (2016)Efficient SDS Simulations on Multi-GPU Nodes of XSEDE High-End Clusters.S. Schlachter, S. Herbein, M. Taufer, S. Ou, S. Patel, and J. Logan. e-Science, page 116-123. IEEE Computer Society, (2013)Secondary Structure Predictions for Long RNA Sequences Based on Inversion Excursions and MapReduce.D. Yehdego, B. Zhang, V. Kodimala, K. Johnson, M. Taufer, and M. Leung. IPDPS Workshops, page 520-529. IEEE, (2013)

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PRIONN: Predicting Runtime and IO using Neural Networks.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML PRIONN: Predicting Runtime and IO using Neural Networks.

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PRIONN: Predicting Runtime and IO using Neural Networks.