Preparing sparse solvers for exascale computing

Author:

Anzt Hartwig1,Boman Erik2,Falgout Rob3,Ghysels Pieter4,Heroux Michael2ORCID,Li Xiaoye4,Curfman McInnes Lois5,Tran Mills Richard5,Rajamanickam Sivasankaran2,Rupp Karl6,Smith Barry5,Yamazaki Ichitaro2,Meier Yang Ulrike3

Affiliation:

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

2. Sandia National Laboratories, Albuquerque, NM, USA

3. Lawrence Livermore National Laboratory, Livermore, CA, USA

4. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

5. Argonne National Laboratory, Argonne, IL, USA

6. Vienna University of Technology, Wien, Wien, Austria

Abstract

Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

Funder

United States Department of Energy

Publisher

The Royal Society

Subject

General Physics and Astronomy,General Engineering,General Mathematics

Reference70 articles.

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2. Livermore Computing Center. 2019 Sierra Computing System. See https://hpc.llnl.gov/hardware/platforms/sierra.

3. MPI Forum. 2019 Message Passing Interface (MPI). See www.mpi-forum.org.

4. Hornung RD Keasler JA. 2014 The RAJA portability layer: overview and status. Technical Report LLNL-TR-661403 Lawrence Livermore National Laboratory.

5. Kokkos: Enabling manycore performance portability through polymorphic memory access patterns

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