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J. Low Power Electron. Appl. 2018, 8(2), 15; https://doi.org/10.3390/jlpea8020015

Optimization of Finite-Differencing Kernels for Numerical Relativity Applications

1
Dipartimento di Scienze Matematiche Fisiche ed Informatiche, Universitá di Parma, I-43124 Parma, Italia
2
Istituto Nazionale di Fisica Nucleare, Sezione Milano Bicocca, Gruppo Collegato di Parma, I-43124 Parma, Italia
3
Theoretisch-Physikalisches Institut, Friedrich-Schiller-Universität Jena, 07743 Jena, Deutschland
4
Istituto Nazionale di Fisica Nucleare, Sezione Milano Bicocca, I-20126 Milano, Italia
5
Institute for Advanced Study, 1 Einstein Drive, Princeton, NJ 08540, USA
6
Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ 08544, USA
*
Author to whom correspondence should be addressed.
Received: 23 March 2018 / Revised: 23 May 2018 / Accepted: 24 May 2018 / Published: 25 May 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. Our proposed method provides substantial speedup in computations involving tensor contractions and 3D stencil calculations on different processor microarchitectures, including Intel Knight Landing. View Full-Text
Keywords: numerical relativity; many-core architectures; knight landing; vectorization numerical relativity; many-core architectures; knight landing; vectorization
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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.

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