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Title: Basic Energy Sciences Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Basic Energy Sciences, November 3-5, 2015, Rockville, Maryland

Technical Report ·
DOI:https://doi.org/10.2172/1341721· OSTI ID:1341721
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  1. Ames Lab., Ames, IA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  5. Argonne National Lab. (ANL), Argonne, IL (United States)
  6. Energy Sciences Network (ESNet), Berkeley, CA (United States)
  7. Univ. of Texas, El Paso, TX (United States)
  8. Princeton Univ., NJ (United States)
  9. Univ. of Illinois, Urbana-Champaign, IL (United States)
  10. Univ. of Texas, Austin, TX (United States)
  11. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  12. Univ. of Florida, Gainesville, FL (United States)
  13. Washington State Univ., Pullman, WA (United States)
  14. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
  15. Univ. of Alabama, Tuscaloosa, AL (United States)
  16. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  17. Stony Brook Univ., NY (United States)
  18. Georgetown Univ., Washington, DC (United States)
  19. Univ. of Minnesota, Minneapolis, MN (United States)
  20. Univ. of Chicago, IL (United States)
  21. Iowa State Univ., Ames, IA (United States)
  22. Univ. of Michigan, Ann Arbor, MI (United States)
  23. Univ. of California, Davis, CA (United States)
  24. Univ. of California, Merced, CA (United States)
  25. Louisiana State Univ., Baton Rouge, LA (United States)
  26. Univ. of Southern California, Los Angeles, CA (United States)
  27. Indian Inst. of Science, Bangalore (India)
  28. Univ. of Washington, Seattle, WA (United States)
  29. Stanford Univ., CA (United States)
  30. Columbia Univ., New York, NY (United States)
  31. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  32. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  33. Univ. of California, Berkeley, CA (United States)
  34. Purdue Univ., West Lafayette, IN (United States)
  35. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
+ Show Author Affiliations

Computers have revolutionized every aspect of our lives. Yet in science, the most tantalizing applications of computing lie just beyond our reach. The current quest to build an exascale computer with one thousand times the capability of today’s fastest machines (and more than a million times that of a laptop) will take researchers over the next horizon. The field of materials, chemical reactions, and compounds is inherently complex. Imagine millions of new materials with new functionalities waiting to be discovered — while researchers also seek to extend those materials that are known to a dizzying number of new forms. We could translate massive amounts of data from high precision experiments into new understanding through data mining and analysis. We could have at our disposal the ability to predict the properties of these materials, to follow their transformations during reactions on an atom-by-atom basis, and to discover completely new chemical pathways or physical states of matter. Extending these predictions from the nanoscale to the mesoscale, from the ultrafast world of reactions to long-time simulations to predict the lifetime performance of materials, and to the discovery of new materials and processes will have a profound impact on energy technology. In addition, discovery of new materials is vital to move computing beyond Moore’s law. To realize this vision, more than hardware is needed. New algorithms to take advantage of the increase in computing power, new programming paradigms, and new ways of mining massive data sets are needed as well. This report summarizes the opportunities and the requisite computing ecosystem needed to realize the potential before us. In addition to pursuing new and more complete physical models and theoretical frameworks, this review found that the following broadly grouped areas relevant to the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR) would directly affect the Basic Energy Sciences (BES) mission need. Simulation, visualization, and data analysis are crucial for advances in energy science and technology. Revolutionary mathematical, software, and algorithm developments are required in all areas of BES science to take advantage of exascale computing architectures and to meet data analysis, management, and workflow needs. In partnership with ASCR, BES has an emerging and pressing need to develop new and disruptive capabilities in data science. More capable and larger high-performance computing (HPC) and data ecosystems are required to support priority research in BES. Continued success in BES research requires developing the next-generation workforce through education and training and by providing sustained career opportunities.

Research Organization:
US Department of Energy, Washington, DC (United States). Advanced Scientific Computing Research and Basic Energy Sciences
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI ID:
1341721
Country of Publication:
United States
Language:
English

Linked Research (2)

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97 MATHEMATICS AND COMPUTING