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Title: Extreme Heterogeneity 2018: Productive Computational Science in the Era of Extreme Heterogeneity Report for DOE ASCR Basic Research Needs Workshop on Extreme Heterogeneity January 23–25, 2018

Technical Report ·
DOI:https://doi.org/10.2172/1473756· OSTI ID:1494112
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  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
  3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  5. Argonne National Lab. (ANL), Argonne, IL (United States)
  6. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  7. Brookhaven National Lab. (BNL), Upton, NY (United States)
  8. Stanford Univ., CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
  9. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
  10. Univ. of Utah, Salt Lake City, UT (United States)
  11. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
  12. Illinois Inst. of Technology, Chicago, IL (United States)
  13. Tactical Computing Lab. LLC, Muenster, TX (United States)
  14. Univ. of Southern California, Los Angeles, CA (United States). Information Sciences Institute (ISI)
  15. Rice Univ., Houston, TX (United States)
  16. Univ. of Arizona, Tucson, AZ (United States)
+ Show Author Affiliations

Providing a software environment that can overcome the complexities of changes in how future supercomputers will be designed plays a key role in improving the nation’s rate of scientific discovery and innovation. In the past three decades, advances in computer technology have allowed the performance and functionality of processors to double every 2 years. This trend, known as Moore’s Law, has enabled both computational and experimental science to leverage the so far unending growth of the broad computing industry with very little change to the supporting software environment. But as computer chip manufacturing techniques reach the limits of the atomic scale, this era of predictable improvements is ending. This shift will have a significant impact on the design of high-performance computers, as well as the established software infrastructure required to effectively utilize the nation’s Leadership Computing Facilities. Computer vendors are pursuing systems built from combinations of different types of processors to improve capabilities, boost performance, and meet energy efficiency goals. Some of the most current supercomputers do not rely on a single type of processor but instead have added computational accelerators to meet the growing demands of increasingly complex computational workloads. According to studies from the US Department of Energy (DOE) Office of Science Advanced Scientific Computing Research program, several types of special-purpose accelerated processing units are currently under development and will play a huge role in the future of computer architectures. It is also likely that these processors will be augmented with diverse types of memory and data storage capabilities. These significant changes are driven by extreme growth in the data-centric machine learning and artificial intelligence marketplaces that far exceed the revenues represented by high-performance computing for computational and experimental science. In the 2025–2030 time frame, external economic drivers and design diversity will result in systems built from a custom aggregation of components; and the difficulty and complexity of developing scientific software will increase. This fundamental change in computer architecture design has been deemed the era of “extreme heterogeneity.” The 2018 Basic Research Needs Workshop on Extreme Heterogeneity identified five Priority Research Directions for realizing the capabilities needed to address the challenges posed in this era of rapid technological change.

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:
1494112
Country of Publication:
United States
Language:
English

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