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Data-Intensive Science in the US DOE: Case Studies and Future Challenges
Nov.-Dec. 2011 (vol. 13 no. 6)
pp. 14-24
James Ahrens, Los Alamos National Laboratory
Bruce Hendrickson, Sandia National Labs
Gabrielle Long, Argonne National Laboratory
Steve Miller, Oak Ridge National Laboratory
Rob Ross, Argonne National Laboratory
Dean Williams, Lawrence Livermore National Laboratory

Given its leading role in high-performance computing for modeling and simulation and its many experimental facilities, the US Department of Energy has a tremendous need for data-intensive science. Locating the challenges and commonalities among three case studies illuminates, in detail, the technical challenges involved in realizing data-intensive science.

1. S. Solomon et al., eds., Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007, Cambridge Univ. Press, encontents.html.
2. W. Washington et al., Scientific Grand Challenges: Challenges in Climate Change Science and the Role of Computing at the Extreme Scale, US Dept. Energy, 2008; ClimateReport.pdf.
3. B. Allcock et al., ASCR Science Requirements, report, US Office of Advanced Scientific Computing Research Network Requirements Workshop, 2009; ASCR-Net-Req-Workshop-2009-Final-Report.pdf .
4. B. Fultz, K. Herwig, and G. Long, Computational Scattering Science 2010, Argonne Nat'l Lab., 2010, .

Index Terms:
Data-intensive science, high-performance computing, data management, scientific computing
James Ahrens, Bruce Hendrickson, Gabrielle Long, Steve Miller, Rob Ross, Dean Williams, "Data-Intensive Science in the US DOE: Case Studies and Future Challenges," Computing in Science and Engineering, vol. 13, no. 6, pp. 14-24, Nov.-Dec. 2011, doi:10.1109/MCSE.2011.77
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