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Title: ASCR/HEP Exascale Requirements Review Report

Program Document ·
OSTI ID:1398463
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  1. Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  5. Univ. of Nebraska, Lincoln, NE (United States)
  6. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  7. Boston Univ., MA (United States)
  8. Indiana Univ., Bloomington, IN (United States)
  9. Univ. of Pittsburgh, PA (United States)
  10. Univ. of California, Los Angeles, CA (United States)
  11. Michigan State Univ., East Lansing, MI (United States)
  12. Yale Univ., New Haven, CT (United States)
  13. Univ. of Illinois, Urbana-Champaign, IL (United States). National Center for Supercomputing Applications (NCSA)
  14. Florida State Univ., Tallahassee, FL (United States)
  15. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  16. Univ. of Arizona, Tucson, AZ (United States)
  17. Brookhaven National Lab. (BNL), Upton, NY (United States)
  18. Univ. of California, San Diego, CA (United States)
+ Show Author Affiliations

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.

Research Organization:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
DOE Contract Number:
AC02-05CH11231
OSTI ID:
1398463
Country of Publication:
United States
Language:
English

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