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.
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.
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.
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