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Title: Exascale computing and big data

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:
Grant/Contract Number:
FG02-13ER26151; ACI-1349521; ACI-1339822
Type:
Accepted Manuscript
Journal Name:
Communications of the ACM
Additional Journal Information:
Journal Volume: 58; Journal Issue: 7; Journal ID: ISSN 0001-0782
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 Orgs:
Univ. of Manchester (United Kingdom)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING
OSTI Identifier:
1361292