Towards Run Time Estimation of the Gaussian Chemistry Code for SEAGrid Science Gateway-Reference-Cited by-同舟云学术

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Towards Run Time Estimation of the Gaussian Chemistry Code for SEAGrid Science Gateway

Published:2019-07-28 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)
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Author:

Beltre Angel¹,Zaman Shehtab¹,Chiu Kenneth¹,Pamidighantam Sudhakar²,Qiao Xingye³,Govindaraju Madhusudhan¹

Affiliation:

1. Department of Computer Science, Binghamton University SUNY, Binghamton, New York, U.S.A.

2. Science Gateways Research Center, Pervasive technology Institute and Department of Chemistry, Indiana University, Bloomington, Indiana, U.S.A.

3. Department of Mathematical Sciences, Binghamton University SUNY, Binghamton, New York, U.S.A.

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3332186.3338101

Reference36 articles.

1. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado and Andy Davis year=2015. {n. d.}. TensorFlow: DNNRegressor. ({n. d.}). https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/estimator/DNNRegressor Software available from tensorflow.org. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado and Andy Davis year=2015. {n. d.}. TensorFlow: DNNRegressor. ({n. d.}). https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/estimator/DNNRegressor Software available from tensorflow.org.

2. Modelling the Runtime of the Gaussian Computational Chemistry Application and Assessing the Impacts of Microarchitectural Variations

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