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

Kwai Wong, Stanimire Tomov, Jack Dongarra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationHigh Performance Computing - ISC High Performance 2019 International Workshops, Revised Selected Papers
EditorsMichèle Weiland, Guido Juckeland, Sadaf Alam, Heike Jagode
PublisherSpringer
Pages643-655
Number of pages13
ISBN (Print)9783030343552
DOIs
StatePublished - 2019
Event34th International Conference on High Performance Computing, ISC High Performance 2019 - Frankfurt, Germany
Duration: Jun 16 2019Jun 20 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11887 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference34th International Conference on High Performance Computing, ISC High Performance 2019
Country/TerritoryGermany
CityFrankfurt
Period06/16/1906/20/19

Funding

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.

FundersFunder number
National Institute for Computational SciencesTG-ASC170031, ACI-1548562
National Science Foundation1659502, 1262937
University of Tennessee

    Keywords

    • Computational science
    • Data analytics
    • Educational outreach
    • HPC
    • Hands-on experiences and education
    • Machine learning (ML)
    • Research Experiences for Undergraduates

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