Preprint
Article

Optimization of Finite-Differencing Kernels for Numerical Relativity Applications

This version is not peer-reviewed.

Submitted:

23 March 2018

Posted:

26 March 2018

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Abstract
A simple optimization strategy for the computation of 3D finite-differencing kernels on many-cores architectures is proposed. The 3D finite-differencing computation is split direction-by-direction and exploits two level of parallelism: in-core vectorization and multi-threads shared-memory parallelization. The main application of this method is to accelerate the high-order stencil computations in numerical relativity codes.
Keywords: 
numerical relativity; many-core architectures; Knight Landing; vectorization
Subject: 
Physical Sciences  -   Astronomy and Astrophysics
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

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