Optimization of Finite-Differencing Kernels for Numerical Relativity Applications

Optimization of Finite-Differencing Kernels for Numerical Relativity Applications

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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 kinkalicious 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 domeless quartz nails codes.Our proposed method provides substantial speedup in computations involving tensor contractions and 3D stencil calculations on different processor microarchitectures, including Intel Knight Landing.