Optimization of Finite-Differencing Kernels for Numerical Relativity Applications

Optimization of Finite-Differencing Kernels for Numerical Relativity Applications

Blog Article

A simple optimization strategy for the computation of 3D finite-differencing kernels on Swaddle 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 70s Loose Flare 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.