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Preparing sparse solvers for exascale computing.

Hartwig Anzt1, Erik Boman2, Rob Falgout3

  • 1Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|January 21, 2020
PubMed
Summary
This summary is machine-generated.

Developing highly parallel sparse solvers is crucial for exascale computing. This work details the US Department of Energy

Keywords:
mathematical librariessparse solvers

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Area of Science:

  • Computational Science
  • Applied Mathematics
  • Computer Science

Background:

  • Sparse solvers are fundamental to scientific computing.
  • Exascale platforms demand highly parallel solvers for complex simulations.
  • Current computational challenges hinder high-fidelity, multi-physics, and multi-scale modeling.

Purpose of the Study:

  • To outline the challenges and strategies for developing sparse solvers for exascale systems.
  • To report on the progress of the US Department of Energy Exascale Computing project.
  • To address the specific demands of massively parallel computing architectures.

Main Methods:

  • Investigating strategies for exposing concurrency in sparse solvers.
  • Developing techniques to hide latency in parallel computations.
  • Exploring alternative algorithmic approaches for exascale environments.

Main Results:

  • Progress has been made in adapting sparse solvers for exascale platforms.
  • Current successes and ongoing challenges in parallel solver development are highlighted.
  • Strategies for managing thousands of high-performance node devices are being refined.

Conclusions:

  • The development of sparse solvers for exascale computing is an ongoing effort.
  • Addressing concurrency and latency is essential for future high-performance simulations.
  • Continued research is needed to overcome upcoming challenges in exascale solver technology.