Preprint Article Version 1 This version is not peer-reviewed

Optimization of Finite-Differencing Kernels for Numerical Relativity Applications

Version 1 : Received: 23 March 2018 / Approved: 26 March 2018 / Online: 26 March 2018 (07:53:06 CEST)

A peer-reviewed article of this Preprint also exists.

Alfieri, R.; Bernuzzi, S.; Perego, A.; Radice, D. Optimization of Finite-Differencing Kernels for Numerical Relativity Applications. J. Low Power Electron. Appl. 2018, 8, 15. Alfieri, R.; Bernuzzi, S.; Perego, A.; Radice, D. Optimization of Finite-Differencing Kernels for Numerical Relativity Applications. J. Low Power Electron. Appl. 2018, 8, 15.

Journal reference: J. Low Power Electron. Appl. 2018, 8, 15
DOI: 10.3390/jlpea8020015

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.

Subject Areas

numerical relativity; many-core architectures; Knight Landing; vectorization

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