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.

Optimization of Finite-Differencing Kernels for Numerical Relativity Applications / Alfieri, Roberto; Bernuzzi, Sebastiano; Perego, Albino; Radice, David. - In: JOURNAL OF LOW POWER ELECTRONICS AND APPLICATIONS. - ISSN 2079-9268. - 8:2(2018). [10.3390/jlpea8020015]

Optimization of Finite-Differencing Kernels for Numerical Relativity Applications

Roberto Alfieri
;
Sebastiano Bernuzzi;Albino Perego;
2018

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2847634
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