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A new Algorithm Based on Factorization for Heterogeneous Domain Decomposition
Often computational models are too expensive to be solved in the entire
domain of simulation, and a cheaper model would suffice away from the main zone
of interest. We present for the concrete example of an evolution problem of
advection reaction diffusion type a heterogeneous domain decomposition
algorithm which allows us to recover a solution that is very close to the
solution of the fully viscous problem, but solves only an inviscid problem in
parts of the domain. Our new algorithm is based on the factorization of the
underlying differential operator, and we therefore call it factorization
algorithm. We give a detailed error analysis, and show that we can obtain
approximations in the viscous region which are much closer to the viscous
solution in the entire domain of simulation than approximations obtained by
other heterogeneous domain decomposition algorithms from the literature.Comment: 23 page
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Preparing sparse solvers for exascale computing.
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'
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