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Efficient exascale discretizations: High-order finite element methods

Journal Article · · International Journal of High Performance Computing Applications
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  1. Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, USA
  2. Mathematics and Computer Science, Argonne National Laboratory, Lemont, IL, USA, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA, Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
  3. Mathematics and Computer Science, Argonne National Laboratory, Lemont, IL, USA
  4. Innovative Computing Laboratory, University of Tennessee, Knoxville, TN, USA
  5. Department of Computer Science, University of Colorado, Boulder, CO, USA
  6. Department of Mathematics, Virginia Tech, Blacksburg, VA, USA
  7. Scientific Computation Research Center, Rensselaer Polytechnic Institute, Troy, NY, USA
  8. AMD Research, Austin, TX, USA
  9. Mechanical Engineering Department, Middle East Technical University, Ankara, Turkey
  10. Occalytics LLC, Weehawken, NJ, USA
  11. Mathematics and Computer Science, Argonne National Laboratory, Lemont, IL, USA, Department of Nuclear Engineering, Penn State, PA, USA
  12. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
  13. Pacific Northwest National Laboratory, WA, USA
  14. Mathematics and Computer Science, Argonne National Laboratory, Lemont, IL, USA, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Greece

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

Sponsoring Organization:
USDOE
OSTI ID:
1787877
Alternate ID(s):
OSTI ID: 1864159
Journal Information:
International Journal of High Performance Computing Applications, Journal Name: International Journal of High Performance Computing Applications Journal Issue: 6 Vol. 35; ISSN 1094-3420
Publisher:
SAGE PublicationsCopyright Statement
Country of Publication:
United States
Language:
English

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