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Title: Exascale computing and big data

Abstract

Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics. The tools and cultures of high-performance computing and big data analytics have diverged, to the detriment of both; unification is essential to address a spectrum of major research domains. The challenges of scale tax our ability to transmit data, compute complicated functions on that data, or store a substantial part of it; new approaches are required to meet these challenges. Finally, the international nature of science demands further development of advanced computer architectures and global standards for processing data, even as international competition complicates the openness of the scientific process.

Authors:
 [1];  [2]
  1. Univ. of Iowa, Iowa City, IA (United States)
  2. Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Manchester (United Kingdom)
Publication Date:
Research Org.:
Univ. of Iowa, Iowa City, IA (United States); Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF)
Contributing Org.:
Univ. of Manchester (United Kingdom)
OSTI Identifier:
1361292
Grant/Contract Number:  
FG02-13ER26151; ACI-1349521; ACI-1339822
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Communications of the ACM
Additional Journal Information:
Journal Volume: 58; Journal Issue: 7; Journal ID: ISSN 0001-0782
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Reed, Daniel A., and Dongarra, Jack. Exascale computing and big data. United States: N. p., 2015. Web. doi:10.1145/2699414.
Reed, Daniel A., & Dongarra, Jack. Exascale computing and big data. United States. doi:10.1145/2699414.
Reed, Daniel A., and Dongarra, Jack. Thu . "Exascale computing and big data". United States. doi:10.1145/2699414. https://www.osti.gov/servlets/purl/1361292.
@article{osti_1361292,
title = {Exascale computing and big data},
author = {Reed, Daniel A. and Dongarra, Jack},
abstractNote = {Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics. The tools and cultures of high-performance computing and big data analytics have diverged, to the detriment of both; unification is essential to address a spectrum of major research domains. The challenges of scale tax our ability to transmit data, compute complicated functions on that data, or store a substantial part of it; new approaches are required to meet these challenges. Finally, the international nature of science demands further development of advanced computer architectures and global standards for processing data, even as international competition complicates the openness of the scientific process.},
doi = {10.1145/2699414},
journal = {Communications of the ACM},
issn = {0001-0782},
number = 7,
volume = 58,
place = {United States},
year = {2015},
month = {6}
}

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Cited by: 53 works
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    journal, June 2018

    • Lee, Sung Min; Yoon, Su-Kyung; Kim, Jeong-Geun
    • The Journal of Supercomputing, Vol. 74, Issue 9
    • DOI: 10.1007/s11227-018-2466-7

    Optimized combinatorial clustering for stochastic processes
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    Adaptive correlated prefetch with large-scale hybrid memory system for stream processing
    journal, June 2018

    • Lee, Sung Min; Yoon, Su-Kyung; Kim, Jeong-Geun
    • The Journal of Supercomputing, Vol. 74, Issue 9
    • DOI: 10.1007/s11227-018-2466-7