DataStates: Towards Lightweight Data Models for Deep Learning
A key emerging pattern in deep learning applications is the need to capture intermediate DNN model snapshots and preserve or clone them in explore a large number of alternative training and/or inference paths. However, with increasing model complexity and
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omputer and Information Science
Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020 Oak Ridge, TN, USA, August 26-28, 2020 Revised Selected Papers
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Communications in Computer and Information Science Editorial Board Members Joaquim Filipe Polytechnic Institute of Setúbal, Setúbal, Portugal Ashish Ghosh Indian Statistical Institute, Kolkata, India Raquel Oliveira Prates Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil Lizhu Zhou Tsinghua University, Beijing, China
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More information about this series at http://www.springer.com/series/7899
Jeffrey Nichols Becky Verastegui Arthur ‘Barney’ Maccabe Oscar Hernandez Suzanne Parete-Koon Theresa Ahearn (Eds.) •
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Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020 Oak Ridge, TN, USA, August 26–28, 2020 Revised Selected Papers
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Editors Jeffrey Nichols Oak Ridge National Laboratory Oak Ridge, TN, USA
Becky Verastegui Oak Ridge National Laboratory Oak Ridge, TN, USA
Arthur ‘Barney’ Maccabe Oak Ridge National Laboratory Oak Ridge, TN, USA
Oscar Hernandez Oak Ridge National Laboratory Oak Ridge, TN, USA
Suzanne Parete-Koon Oak Ridge National Laboratory Oak Ridge, TN, USA
Theresa Ahearn Oak Ridge National Laboratory Oak Ridge, TN, USA
ISSN 1865-0929 ISSN 1865-0937 (electronic) Communications in Computer and Information Science ISBN 978-3-030-63392-9 ISBN 978-3-030-63393-6 (eBook) https://doi.org/10.1007/978-3-030-63393-6 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Ge