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Hands-On Research and Training in High Performance Data Sciences, Data Analytics, and Machine Learning for Emerging Environments

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High Performance Computing (ISC High Performance 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11887))

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Abstract

This paper describes a hands-on Research Experiences for Computational Science, Engineering, and Mathematics (RECSEM) program in high-performance data sciences, data analytics, and machine learning on emerging computer architectures. RECSEM is a Research Experiences for Undergraduates (REU) site program supported by the USA National Science Foundation. This site program at the University of Tennessee (UTK) directs a group of ten undergraduate students to explore, as well as contribute to the emergent interdisciplinary computational science models and state-of-the-art HPC techniques via a number of cohesive compute and data intensive applications in which numerical linear algebra is the fundamental building block.

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References

  1. Betancourt, F., Wong, K., Asemota, E., Marshall, Q., Nichols, D., Tomov, S.: OpenDIEL: a parallel workflow engine and data analytics framework. In: Practice and Experience in Advanced Research Computing (PEARC 2019), July 2019. ACM, Chicago (2019)

    Google Scholar 

  2. Chen, S., Gessinger, A., Tomov, S.: Design and acceleration of convolutional neural networks on modern architectures. Technical report, Joint Institute for Computational Sciences (JICS), UTK, 2018 Summer Research Experiences for Undergraduate (REU), Knoxville, TN, p. 2018 (2018)

    Google Scholar 

  3. Cheng, X., Sorna, A., D’Azevedo, E., Wong, K., Tomov, S.: Accelerating 2D FFT: Exploit GPU Tensor Cores through Mixed-Precision, November 2018 (2018)

    Google Scholar 

  4. Innovative Computing Laboratory (ICL). http://icl.cs.utk.edu

  5. Ng, L., et al.: MagmaDNN 0.2: high-performance data analytics for manycore GPUs and CPUs. In: MagmaDNN, 2018 Summer Research Experiences for Undergraduate (REU), January 2019

    Google Scholar 

  6. Ng, L., Wong, K., Haidar, A., Tomov, S., Dongarra, J.: MagmaDNN - high-performance data analytics for manycore GPUs and CPUs. In: MagmaDNN, 2017 Summer Research Experiences for Undergraduate (REU) (2017)

    Google Scholar 

  7. Nichols, D., Tomov, N.S., Betancourt, F., Tomov, S., Wong, K., Dongarra, J.: MagmaDNN: towards high-performance data analytics and machine learning for data-driven scientific computing. In: ISC High Performance, June 2019. Springer, Frankfurt (2019)

    Google Scholar 

  8. Nichols, D., Wong, K., Tomov, S., Ng, L., Chen, S., Gessinger, A.: MagmaDNN: accelerated deep learning using MAGMA. In: Practice and Experience in Advanced Research Computing (PEARC 2019), July 2019. ACM, Chicago (2019)

    Google Scholar 

  9. RECSEM REU Program at UTK. http://www.jics.utk.edu/recsem-reu

  10. Research Experience for Undergraduates (REU): Socially Relevant Computing. https://reu.uncc.edu/cise-reu-toolkit/results-cise-reu-toolkit

  11. Computational Science for Undergraduate Research Experience, 2013–17 internal reports. http://www.jics.utk.edu/csure-reu/csure13/projects, http://www.jics.utk.edu/cure-reu/csure-14/projects, http://www.jics.utk.edu/csure-reu/csure15/projects, http://www.jics.utk.edu/csure-reu/csure16/projects, http://www.jics.utk.edu/recsem-reu/recsem17/projects

  12. Sorna, A., Cheng, X., D’Azevedo, E., Wong, K., Tomov, S.: Optimizing the fast Fourier transform using mixed precision on tensor core hardware. In: 2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW), pp. 3–7, December 2018. https://doi.org/10.1109/HiPCW.2018.8634417

  13. Tomov, S., Dongarra, J., Baboulin, M.: Towards dense linear algebra for hybrid GPU accelerated manycore systems. Parellel Comput. Syst. Appl. 36(5–6), 232–240 (2010). https://doi.org/10.1016/j.parco.2009.12.005

    Article  MATH  Google Scholar 

  14. Towns, J. et al.: XSEDE: accelerating scientific discovery. Comput. Sci. Eng. 16(5), 62–74 (2014). https://doi.org/10.1109/MCSE.2014.80

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Acknowledgments

This work was conducted at the Joint Institute for Computational Sciences (JICS), sponsored by the National Science Foundation (NSF), through NSF REU Award #1262937 and #1659502, with additional Support from the University of Tennessee, Knoxville (UTK), and the National Institute for Computational Sciences (NICS). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Computational Resources are available through a XSEDE education allocation award TG-ASC170031.

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Authors and Affiliations

  1. University of Tennessee, Knoxville, TN, 37996, USA

    Kwai Wong, Stanimire Tomov & Jack Dongarra

  2. Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA

    Jack Dongarra

Authors
  1. Kwai Wong
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  2. Stanimire Tomov
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  3. Jack Dongarra
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Corresponding author

Correspondence to Stanimire Tomov .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Michèle Weiland

  2. Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Sachsen, Germany

    Guido Juckeland

  3. Swiss National Supercomputing Centre, Lugano, Ticino, Switzerland

    Sadaf Alam

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Heike Jagode

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Wong, K., Tomov, S., Dongarra, J. (2019). Hands-On Research and Training in High Performance Data Sciences, Data Analytics, and Machine Learning for Emerging Environments. In: Weiland, M., Juckeland, G., Alam, S., Jagode, H. (eds) High Performance Computing. ISC High Performance 2019. Lecture Notes in Computer Science(), vol 11887. Springer, Cham. https://doi.org/10.1007/978-3-030-34356-9_49

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  • DOI: https://doi.org/10.1007/978-3-030-34356-9_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34355-2

  • Online ISBN: 978-3-030-34356-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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